Theory and Practice of Machine Learning and Data Analysis

Vadim V. Strijov, DSc, Principal Investigator at the Computing Center of the Russian Academy of Sciences, Associate Professor at the Chair of Intelligent Systems of the Moscow Institute of Physics and Technology, Editor in chief of the "Journal of Machine Learning and Data Analysis".

E-mail:
Phone: +7(499)135-4163
Web: machinelearning.ru, strijov.com

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Vadim Strijov

List of papers

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2017

Rudakov K.V., Kuznetsov M.P., Motrenko A.P., Stenina M.M., Kashirin D.O., Strijov V.V. Optimal model selection for rail freight forecasting // Automatics and Telemechanics, 2017, 78(1) : 74-86. Article
Abstract: Решается задача выбора оптимальной модели краткосрочного прогнозирования объемов железнодорожных перевозок по историческим и экзогенным временным рядам. Исторические данные содержат информацию об объемах перевозок различных типов грузов между парами станций. Предполагается, что результат выбора оптимальной модели зависит от уровня агрегирования по типам грузов, пунктам отправления и назначения и по времени. Рассмотрены модели векторной авторегрессии, интегрированная модель авторегрессионного скользящего среднего и непараметрическая модель гистограммного прогнозирования. Предложены критерии сравнения прогнозов на основе расстояний между ошибками прогнозов моделей. Данные критерии используются для анализа моделей с целью определения допустимых запросов на прогноз, в том числе, фактической глубины прогнозирования.
BibTeX:
 
@article{Rudakov2015RZD, 
  author = {Rudakov, K. V. and Kuznetsov, M. P. and Motrenko, A. P. and Stenina, M. M. and Kashirin, D. O. and Strijov, V. V.},
  title = {Optimal model selection for rail freight forecasting},
  journal = {Automatics and Telemechanics},
  year = {2017},
  volume = {78(1)},
  pages = {74-86},
  url = {http://strijov.com/papers/Rudakov2015RZD.pdf},
  doi = {10.1134/S0005117917010064}
}

2016

Bakhteev O.Y., Popova M.S., Strijov V.V. Systems and means of deep learning for classification problems // Systems and Means of Informatics, 2016, 26(2) : 4-22. Article
Abstract: The paper provides a guidance on deep learning net construction and optimization using GPU. The paper proposes to use GPU-instances on the cloud platform Amazon Web Services. The problem of time series classification is considered. The paper proposes to use a deep learning net, i.e. a multilevel superposition of models, belonging to the following classes: Restricted Boltzman Machines, autoencoders and neural nets with softmax-function in output. The proposed method was tested on a dataset containing time segments from mobile phone accelerometer. The analysis of relation between classification error, dataset size and superposition parameter amount is conducted.
BibTeX:
 
@article{Bakhteev2016AWS, 
  author = {Bakhteev, O. Yu. and Popova, M. S. and Strijov, V. V.},
  title = {Systems and means of deep learning for classification problems},
  journal = {Systems and Means of Informatics},
  year = {2016},
  volume = {26(2)},
  pages = {4-22},
  url = {http://strijov.com/papers/Bakhteev2016AWS.pdf}
}
Goncharov A.V., Strijov V.V. Metric time series classification using weighted dynamic warping relative to centroids of classes // Informatics and Applications, 2016, 10(2) : 36-47. Article
Abstract: This paper discusses a problem of metric time series analysis and classification. The proposed classification model uses the matrix of distances between time series which is built with fixed distance function. The dimension of this distance matrix is very high and all related calculations are time-consuming. The problem of reducing the computational complexity is solved by selection reference objects and using them for describing classes. Model that uses dynamic time warping for building reference objects or centroids is chosen as a basic model. This paper introduces a function of weights for each centroid that influence on calculating the distance measure. Time series of different analytic functions and time series of human activity from accelerometer of mobile phone are used as the objects for classification. Properties and classification result of this model are investigated and compared with properties of basic model.
BibTeX:
 
@article{Goncharov2015autumn, 
  author = {Goncharov, A. V. and Strijov, V. V.},
  title = {Metric time series classification using weighted dynamic warping relative to centroids of classes},
  journal = {Informatics and Applications},
  year = {2016},
  volume = {10(2)},
  pages = {36-47},
  url = {http://strijov.com/papers/Goncharov2015authumn.pdf},
  doi = {10.14357/19922264160204}
}
Isachenko R.V., Strijov V.V. Metric learning in multiclass time series classification problem // Informatics and Applications, 2016, 10(2) : 48-57. Article
Abstract: This paper is devoted to the problem of multiclass time series classification. It is proposed to align time series in relation to class centroids. Building of centroids and alignment of time series is carried out by the dynamic time warping algorithm. The accuracy of classification depends significantly on the metric used to compute distances between time series. The distance metric learning approach is used to improve classification accuracy. Themetric learning proceduremodifies distances between objects to make objects fromthe same cluster closer and from the different clusters more distant. The distance between time series is measured by the Mahalanobis metric. The distance metric learning procedure finds the optimal transformation matrix for the Mahalanobis metric. To calculate quality of classification, a computational experiment on synthetic data and real data of human activity recognition was carried out.
BibTeX:
 
@article{Isachenko2016MetricsLearning, 
  author = {Isachenko, R. V. and Strijov, V. V.},
  title = {Metric learning in multiclass time series classification problem},
  journal = {Informatics and Applications},
  year = {2016},
  volume = {10(2)},
  pages = {48-57},
  url = {http://strijov.com/papers/Isachenko2016MetricsLearning.pdf},
  doi = {10.14357/19922264160205}
}
Karasikov M.E., Strijov V.V. Feature-Based Time-Series Classification // Informatics, 2016. Article
Abstract: The paper if devoted to multi-class time-series classification problem. Feature- based approach that uses meaningful and concise representations for feature space con- struction is applied. A time-series is considered as a sequence of segments, approximated by parametric models and their parameters are used as time-series features. This fea- ture construction method inherits from approximation model such unique properties as shift invariance. We propose an approach to solve time-series classification problem using distributions of parameters of approximation model. The proposed approach is applied to human activity classification problem. The computational experiments on real data demonstrate superiority of proposed algorithm over baseline solutions.
BibTeX:
 
@article{Karasikov2016TSC, 
  author = {Karasikov, M. E. and Strijov, V. V.},
  title = {Feature-Based Time-Series Classification},
  journal = {Informatics},
  year = {2016},
  url = {http://strijov.com/papers/Karasikov2016TSC.pdf}
}
Kuznetsov M., Motrenko A., Kuznetsova M., Strijov V.V. Methods for intrinsic plagiarism detection and author diarization // Working Notes of CLEF, 2016, 1609 : 912-919. Article
Abstract: The paper investigates methods for intrinsic plagiarism detection and author diarization. We developed a plagiarism detection method based on constructing an author style function from features of text sentences and detecting outliers. We adapted the method for the diarization problem by segmenting author style statistics on text parts, which correspond to different authors. Both methods were tested on the PAN-2011 collection for the intrinsic plagiarism detection and implemented for the PAN-2016 competition on author diarization.
BibTeX:
 
@article{Kuznetsov2016CLEF, 
  author = {Kuznetsov, M.P. and Motrenko, A.P. and Kuznetsova, M.V. and Strijov, V. V.},
  title = {Methods for intrinsic plagiarism detection and author diarization},
  journal = {Working Notes of CLEF},
  year = {2016},
  volume = {1609},
  pages = {912-919},
  url = {http://ceur-ws.org/Vol-1609/16090912.pdf},
  doi = {http://ceur-ws.org/Vol-1609/}
}
Kuznetsov M.P., Tokmakova A.A., Strijov V.V. Analytic and stochastic methods of structure parameter estimation // Informatica, 2016, 27(3) : 607-624. Article
Abstract: The paper presents analytic and stochastic methods of structure parameters estimation for model selection. Structure parameters are covariance matrices of parameters of linear and non-linear regression models. To optimize the model parameters and the structure parameters we maximize the model evidence including the data likelihood and the prior parameter distribution. The analytic methods are based on the approximated model evidence derivatives computation. The stochastic methods are based on the model parameters sampling and data cross-validation. The proposed methods are tested and compared on synthetic and real data.
BibTeX:
 
@article{Kuznetsov2013Structure, 
  author = {Kuznetsov, M. P. and Tokmakova, A. A. and Strijov, V. V.},
  title = {Analytic and stochastic methods of structure parameter estimation},
  journal = {Informatica},
  year = {2016},
  volume = {27(3)},
  pages = {607-624},
  url = {http://strijov.com/papers/HyperOptimizationEng.pdf},
  doi = {http://www.mii.lt/informatica/pdf/INFO1109.pdf}
}
Kuznetsova M.V., Strijov V.V. Local forecasting of time series with invariant transformations // Information Technologies, 2016, 22(6) : 457-462. Article
Abstract: The paper describes a univariate time series forecasting model. It proposes to find segments of local history, which are similar to the forecasted segment. A distance function is used to cluster segments. The forecast is the average of the value of time series from this cluster. To improve the quality of forecast the paper proposes an invariant transformation of segments. This transformation holds the equivalence of time series respect to clusters. The transformation is a function, constructed by the dynamic time warping procedure. The retrospective forecasting procedure calculates the accuracy of the forecasting model. Accelerometer time series of a person’s motion are used in computational experiment. It compares two constructing forecasting models. The first one clusters segments, the second one uses k-nearest neighbor algorithm to select similar segments.
BibTeX:
 
@article{Kuznetsova2015TimeSeries, 
  author = {Kuznetsova, M. V. and Strijov, V. V.},
  title = {Local forecasting of time series with invariant transformations},
  journal = {Information Technologies},
  year = {2016},
  volume = {22(6)},
  pages = {457-462},
  url = {http://strijov.com/papers/Kuznetsova2015TimeSeries.pdf}
}
Motrenko A.P., Rudakov K.V., Strijov V.V. Combining endogenous and exogenous variables in a special case of non-parametric time series forecasting model // Moscow University Computational Mathematics and Cybernetics, 2016, 40(2) : 71-78. Article
Abstract: We address a problem of increasing quality of forecasting time series by taking into account the information about exogenous factors. Our aim is to improve a special case of non-parametric forecasting algorithm, namely the hist algorithm, derived from quantile regression. The hist minimizes the convolution of a histogram of time series with the loss function. To include exogenous factors into this model we suggest to correct the histogram of endogenous time series, using exogenous time series. We propose to adjust the histogram, using mixtures of conditional histograms as a less sparse alternative to multidimensional histogram and in some cases demonstrate the decrease of loss compared to the basic forecasting algorithm. To the extent of our knowledge, such approach to combining endogenous and exogenous time series is original and has not been proposed yet. The suggested method is illustrated with the data from the Russian Railways.
BibTeX:
 
@article{Motrenko2015ExogenousFactors, 
  author = {Motrenko, A. P. and Rudakov, K. V. and Strijov, V. V.},
  title = {Combining endogenous and exogenous variables in a special case of non-parametric time series forecasting model},
  journal = {Moscow University Computational Mathematics and Cybernetics},
  year = {2016},
  volume = {40(2)},
  pages = {71-78},
  url = {http://strijov.com/papers/Motrenko2015ExogenousFactors.pdf},
  doi = {10.3103/S0278641916020072}
}
Neychev R.G., Katrutsa A.M., Strijov V.V. Robust selection of multicollinear features in forecasting // Factory Laboratory, 2016, 82(3) : 68-74. Article
Abstract: This paper considers a problem of constructing a stable forecasting model using feature selection methods. It proposes a multicollinearity detection criterion, which is necessary in the case of excessive number of features. To investigate properties of this criterion, a theorem is stated. It develops the Belsley method. The proposed criterion runs an algorithm to exclude correlated features, reduce dimensionality of the feature space and to obtain robust estimations of the model parameters. The algorithm adds and removes features consequently according to this criterion. The LAD-Lasso algorithm was chosen as the basic to compare with. The computational experiment investigates an hourly-price forecasting curve problem with the proposed and the basic algorithms. The experiment carried out using time series of the German electricity prices.
BibTeX:
 
@article{Neychev2015FeatureSelection, 
  author = {Neychev, R. G. and Katrutsa, A. M. and Strijov, V. V.},
  title = {Robust selection of multicollinear features in forecasting},
  journal = {Factory Laboratory},
  year = {2016},
  volume = {82(3)},
  pages = {68-74},
  url = {http://strijov.com/papers/Neychev2015FeatureSelection.pdf}
}
Zadayanchuk A.I., Popova M.C., Strijov V.V. Selection of optimal physical activity classification model using measurements of accelerometer // Information Technologies, 2016, 22(4) : 313-318. Article
Abstract: This paper solves the problem of selecting optimal stable models for classification of physical activity. We select optimal models from the class of two-layer artificial neural networks. There are three different ways to change structure of neurons: network pruning, network growing, and their combination. We construct models by removing its neurons. Neural networks with insufficient or excess number of neurons have insufficient generalization ability and can make unstable predictions. Proposed genetic algorithm optimizes the neural network structure. The novelty of the work lies in the fact that the probability of removing neurons is determined by the variance of parameters. In the computing experiment, models are generated by optimization two quality criteria — accuracy and stability.
BibTeX:
 
@article{Zadayanchuk2015OptimalNN4, 
  author = {Zadayanchuk, A. I. and Popova, M. C. and Strijov, V. V.},
  title = {Selection of optimal physical activity classification model using measurements of accelerometer},
  journal = {Information Technologies},
  year = {2016},
  volume = {22(4)},
  pages = {313-318},
  url = {http://strijov.com/papers/Zadayanchuk2015OptimalNN4.pdf}
}
Zhuravlev Y.I., Rudakov K.V., Korchagin A.D., Kuznetsov M.P., Motrenko A.P., Stenina M.M., Strijov V.V. Methods for hierarchical time series forecasting // Niotices of the Russian Academy of Sciences, 2016, 86. № 2. С. 138 : 138. Article
Abstract: The papers investigates problems of planning in railway freight transportation under conditions of non-stationary, non-uniform and noisy data. To boost quality of planning it proposes to create an intelligent system, which is based on mathematical models, historical data and expert estimations. The paper describes a project on forecasting system to plan the railway freight transportations following analysis of dependence the freight transportation demand on exogenous factors.
BibTeX:
 
@article{Zhur2016TimeSeries, 
  author = {Zhuravlev, Yu. I. and Rudakov, K. V. and Korchagin, A. D. and Kuznetsov, M. P. and Motrenko, A. P. and Stenina, M. M. and Strijov, V. V.},
  title = {Methods for hierarchical time series forecasting},
  journal = {Niotices of the Russian Academy of Sciences},
  year = {2016},
  volume = {86. № 2. С. 138},
  pages = {138},
  url = {http://strijov.com/papers/Zhuravlev2015RZD.pdf},
  doi = {10.7868/S0869587316020213}
}
Goncharov A.V., Strijov V.V. Continuous time series alignment in human actions recognition // Artificial Intelligence and Natural Language & Information Extraction, Social Media and Web Search FRUCT Conference proceedings // AINL FRUCT: Artificial Intelligence and Natural Language Conference proceedings, 2016. InProceedings
Abstract: Human physical activity monitoring with wearable devices imposes significant restrictions on the processing power and the amount of memory available to the algorithm. Proposed to move from discrete time series representation to its analytical description and analyze them using mathematical models for satisfying these constraints. The work deals with physical activity classification. It uses metric classification algorithm, where the object’s class determined by the distance from this object to the nearest centroid. Paper proposed to approximate all time series with splines and find the distance to the nearest centroid using continuous alignment path. The calculation of distance is performed using analytical transformations.
BibTeX:
 
@inproceedings{Gonchariv2016Fruct, 
  author = {Goncharov, A. V. and Strijov, V. V.},
  title = {Continuous time series alignment in human actions recognition},
  booktitle = {AINL FRUCT: Artificial Intelligence and Natural Language Conference proceedings},
  journal = {Artificial Intelligence and Natural Language & Information Extraction, Social Media and Web Search FRUCT Conference proceedings},
  year = {2016},
  url = {http://strijov.com/papers/Goncharov_Fruct_2016.pdf}
}
Kuzmin A.A., Aduenko A.A., Strijov V.V. Hierarchical thematic modeling of short text collection // Intelligent Data Processing, Conference Proceedings, 2016 : 174-175. InProceedings
Abstract: The aim of this study is to construct and verify a hierarchical thematic model of a short text collection. The present authors consider the ways for metrics learning and features selection. Agglomerative and divisive methods to construct a hierarchical model are compared.
A hierarchical weighted similarity function is suggested for unlabeled data classification. Weights in this function are the importance values of the terms from the collection dictionary. Entropy-based approach is used to estimate these weights according to the expert model. The proposed similarity function is represented as four-level neural network to consider vector representation of the words given by a trained language model. The proposed methods are used to construct an expert system that helps experts to classify unlabeled abstracts of the major conference EURO. The parameters of this model are estimated using expert models of EURO conference from 2006 till 2016. The results are compared with hierarchical multiclass SVM, probabilistic thematic model SuhiPLSA, and hierarchical naive Bayes approach.
Review: The aim of this study is to construct and verify a hierarchical thematic model of a short text collection. The present authors consider the ways for metrics learning and features selection. Agglomerative and divisive methods to construct a hierarchical model are compared.
A hierarchical weighted similarity function is suggested for unlabeled data classification. Weights in this function are the importance values of the terms from the collection dictionary. Entropy-based approach is used to estimate these weights according to the expert model. The proposed similarity function is represented as four-level neural network to consider vector representation of the words given by a trained language model.
The proposed methods are used to construct an expert system that helps experts to classify unlabeled abstracts of the major conference EURO. The parameters of this model are estimated using expert models of EURO conference from 2006 till 2016. The results are compared with hierarchical multiclass SVM, probabilistic thematic model SuhiPLSA, and hierarchical naive Bayes approach.
BibTeX:
 
@inproceedings{Kuzmin2016IDP, 
  author = {Kuzmin, A. A. and Aduenko, A. A. and Strijov, V. V.},
  title = {Hierarchical thematic modeling of short text collection},
  booktitle = {Intelligent Data Processing, Conference Proceedings},
  year = {2016},
  pages = {174-175},
  url = {http://strijov.com/papers/Kuzmin_Modeling_ShortText2016.pdf}
}
Kuzmin A.A., Aduenko A.A., Strijov V.V. Thematic Classification for EURO/IFORS Conference Using Expert Model // 28th European Conference on Operational Research, 2016. InProceedings
Abstract: Every year the program committee of a major conference constructs its scientific program. Some participants take part in invited sessions, but for the majority of participants the PC along with experts have to choose sessions according to their contributed abstracts. To fit an abstract into the current conference programme one has to construct an expert system. It should respect previous conferences structure and use thematic modeling techniques. The conference structure represents a tree. It has abstracts as leaves and areas, streams, sessions as nodes. Abstracts from the previous conferences already have their positions in this structure. To classify a new abstract one can use divisive hierarchical classification methods, based on SVM, NB or kNN. However, these methods are greedy. Insufficient number of abstracts in each lowest level cluster makes classification unstable. In addition, expert and algorithmic classifications differs. So a group of the most relevant clusters is preferable than the best one to meet expert needs. We propose a relevance operator that returns all clusters sorted by their relevance. We consider three ways of constructing such operator using hierarchical multiclass SVM, PLSA with Adaptive Regularization, and proposed weighted hierarchical similarity function. We construct a model of EURO 2010 using expert models of EURO 2012 and 2013 to demonstrate performance of proposed methods.
BibTeX:
 
@inproceedings{KuzminEURO2016, 
  author = {Kuzmin, A. A. and Aduenko, A. A. and Strijov, V. V.},
  title = {Thematic Classification for EURO/IFORS Conference Using Expert Model},
  booktitle = {28th European Conference on Operational Research},
  year = {2016},
  url = {http://strijov.com/papers/KuzminEURO2016.pdf}
}
Motrenko A.P., Neychev R.G., Isachenko R.V., Popova M.S., Gromov A.N., Strijov V.V. Feature generation for multiscale time series forecasting // Intelligent Data Processing, Conference Proceedings, 2016 : 129-130. InProceedings
Abstract: The paper presents a framework for the massive multiscale time series forecast. The focus is on the problem of forecasting behavior of a device within the concept of Internet of things. The device is monitored by a set of sensors, which produces large amount of multiscale time series during its lifespan. These time series have various time scales since distinct sensors produce observations with various frequencies from milliseconds to weeks. The main goal is to predict the observations of a device in a given time range. The authors propose a method of constructing efficient feature description for the corresponding regression problem. The method involves feature generation and dimensionality reduction procedures. Generated features include historical information about the target time series as well as other available time series, local transformations, and multiscale features. Several forecasting algorithms have been applied to the resulting regression problem and the quality of the forecasts has been investigated for various horizon values.
BibTeX:
 
@inproceedings{MotrenkoMiltiscale2016IDP, 
  author = {Motrenko, A. P. and Neychev, R. G. and Isachenko, R. V. and Popova, M. S. and Gromov, A. N. and Strijov, V. V.},
  title = {Feature generation for multiscale time series forecasting},
  booktitle = {Intelligent Data Processing, Conference Proceedings},
  year = {2016},
  pages = {129-130},
  url = {https://sourceforge.net/p/mvr/code/HEAD/tree/lectures/DataFest/Strijov2016FeatureGeneration.pdf?format=raw}
}
Neychev R.G., Motrenko A.P., Isachenko R.V., Inyakin A.S., Strijov V.V. Multimodel forecasting multiscale time series in Internet of things // Intelligent Data Processing, 2016 : 130-131. InProceedings
Abstract: The paper presents an approach to forecasting multiple intercorrelated time series that can be generated by different sensors of devices within a concept of Internet of things. In this case, generated data are not independent and identically-distributed and there feature space has a complex structure.
The forecast construction is considered as regression problem. To solve it, the authors propose mixture of experts approach where several forecasting models are used. Neural networks are chosen as the forecasting models. The optimal structure of neural networks, their parameters, and quantity of experts are analyzed. The proposed method has been tested within computational experiment where it was compared to gradient boosting and decision tree methods. The experiment was conducted on real data containing information about electricity consumption and weather conditions in Poland.
BibTeX:
 
@inproceedings{Neychev2016IDP, 
  author = {Neychev, R. G. and Motrenko, A. P. and Isachenko, R. V. and Inyakin, A. S. and Strijov, V. V.},
  title = {Multimodel forecasting multiscale time series in Internet of things},
  booktitle = {Intelligent Data Processing},
  year = {2016},
  pages = {130-131},
  url = {http://www.machinelearning.ru/wiki/images/9/94/NeychevIDP11.pdf}
}
Strijov V.V., Motrenko A.P. Large-scale time series forecasting // 28th European Conference on Operational Research // 28th European Conference on Operational Research, 2016. InProceedings
Abstract: The talk is devoted to investigation of behavior of a device, a member of the internet of things. A device is monitored by a set of sensors, which produces large amount of multiscale time series during its lifespan. These time series have various time scales, due to measurements could perform over each millisecond, day, week, etc. The main goal is to forecast the next state of a device. The investigation assumes the following conditions for a single device unit time series: there are large set of multiscale time series; the sampling rate of a time series is fixed; each time series has its own forecast horizon. To make an adequate forecasting model hold the following hypothesis: the time history is sufficient long; the time series have auto- and cross-correlation dependencies. The model is static, so there exists a history of optimal size. Each time series could be interpolated by some local model, a that there exist a local approximation model, which could be applied in the case of local data absence. The vector-autoregression approach conducts problem statement. To find a model of optimal complexity a consequent model generation-selection procedure was constructed. The test-bench compares random forest, boosting and mixture of experts.
BibTeX:
 
@inproceedings{Strijov2016MultiscaleForecasting, 
  author = {Strijov, V. V. and Motrenko, A. P.},
  title = {Large-scale time series forecasting},
  booktitle = {28th European Conference on Operational Research},
  journal = {28th European Conference on Operational Research},
  year = {2016},
  url = {http://strijov.com/papers/Strijov2016MultiscaleForecasting.pdf}
}
Vladimirova M.R., Strijov V.V. Bagging of neural networks in multitask classification of biological acivity for nuclear receptors // Intelligent Data Processing, Conference Proceedings, 2016 : 18-19. InProceedings
Abstract: The paper is devoted to the multitask classification problem. The main purpose is building an adequate model to predict whether the object belongs to a particular class, precisely, whether the ligand binds to a specific nuclear receptor. Nuclear receptors are a class of proteins found within cells. These receptors work with other proteins to regulate the expression of specific genes, thereby controlling the development, homeostasis, and metabolism of the organism. The regulation of gene expression generally only happens when a ligand a molecule that affects the receptor’s behavior binds to a nuclear receptor.
Two-layer neural network is used as a classification model. The paper considers the problems of linear and logistic regressions with squared and cross-entropy loss functions. To analyze the classification result, the authors propose to decompose the error into bias and variance terms. To improve the quality of classification by reducing the error variance, the authors suggest the composition of neural networks  bagging. Bagging generates a set of subsamples from the training sample using the bootstrap procedure. All subsamples have the same size as initial sample. Classifiers are trained on each subsample separately. Then their individual predictions are aggregated by voting. The proposed method improves the quality of investigated sample classification.
BibTeX:
 
@inproceedings{Vladimorove2016IDP, 
  author = {Vladimirova, M. R. and Strijov, V. V.},
  title = {Bagging of neural networks in multitask classification of biological acivity for nuclear receptors},
  booktitle = {Intelligent Data Processing, Conference Proceedings},
  year = {2016},
  pages = {18-19},
  url = {http://www.machinelearning.ru/wiki/images/5/5f/VladimirovaIOI2016_eng.pdf}
}
Kuznetsov M.P. Construction preference learning models using ordinal-scaled expert estimations (PhD thesis supervised by V.V. Strijov). Moscow Institure of Physics and Technology, 2016. PhdThesis
Abstract: The thesis is devoted to preference learning models. The proposed methods involve rank-scales expert estimations as object features.
BibTeX:
 
@phdthesis{Kuznetsov2016PhDThesis, 
  author = {Kuznetsov, M. P.},
  title = {Construction preference learning models using ordinal-scaled expert estimations (PhD thesis supervised by V.V. Strijov)},
  school = {Moscow Institure of Physics and Technology},
  year = {2016},
  url = {https://mipt.ru/upload/iblock/782/kuznetsov_dissertatsiya.pdf},
  doi = {https://mipt.ru/upload/iblock/3cb/kuznetsov_avtoreferat.pdf}
}

2015

Aduenko A.A., Rudakov K.V., Reyer I.A., Vasileysky A.S., Karelov A., Strijov V.V. Algorithm of detection and registration of persistent scatters on satellite radar images // Computer optics, 2015, 39(4) : 622-630. Article
Abstract: To detect small movements of Earth surface (with a velocity less than several centimeters per year) with use of SAR-interferometry methods it is necessary to find a number of surface areas remaining coherent on radar images over a long period. These areas and corresponding image points are called persistent scatterers. Two methods of persistent scatterers detection are consid-ered in the paper. The methods are compared by the number of detected points and their average time coherence. The algorithms considered are illustrated with an example of processing of a set containing 35 radar images.
BibTeX:
 
@article{Aduenko2015SAR_ComOptics.pdf, 
  author = {Aduenko, A. A. and Rudakov, K. V. and Reyer, I. A. and Vasileysky, A. S. and Karelov, A.I. and Strijov, V. V.},
  title = {Algorithm of detection and registration of persistent scatters on satellite radar images},
  journal = {Computer optics},
  year = {2015},
  volume = {39(4)},
  pages = {622-630},
  url = {http://strijov.com/papers/Aduenko2015PSdetection.pdf},
  doi = {10.18287/0134-2452-2015-39-4-622-630}
}
Gazizullina R.K., Medvednikova M.M., Strijov V.V. Capacity of railway cargo transportation forecasting // Systems and Means of Informatics, 2015, 25(1) : 144-157. Article
Abstract: The article is devoted to research of the algorithm of nonparametric forecasting of railway cargo transportation capacity. The problem considered is forecasting the number of wagons with various goods, following various routes. Topology of the railway network is given - for all possible pairs of railway lines information about all blocks of wagons, which have moved from one line to another, including the number of wagons in a block, type of cargo and date of a route, is provided. The algorithm, based on convolution of empirical density distribution of values ??of time series with loss function, is used for prediction. Previously forecast was carried out for each railway junction separately. Quality of the forecast is proposed to improve due to prediction by pairs of lines instead of predicting departure of all wagons from the given junction. The algorithm is illustrated by daily data on transportation of 38 types of cargo collected during year and a half.
BibTeX:
 
@article{Gazizullina2014RailwayForecasting, 
  author = {Gazizullina, R. K. and Medvednikova, M. M. and Strijov, V. V.},
  title = {Capacity of railway cargo transportation forecasting},
  journal = {Systems and Means of Informatics},
  year = {2015},
  volume = {25(1)},
  pages = {144-157},
  url = {http://strijov.com/papers/Gazizullina2014RailwayForecasting.pdf},
  doi = {10.14357/08696527150109}
}
Goncharov A.V., Popova M.S., Strijov V.V. Metric time series classification using dynamic warping relative to centroids of classes // Systems and Means of Informatics, 2015, 25(4) : 52-64. Article
Abstract: This paper discusses a problem of time series classification in case of several classes. The proposed classification model uses the matrix of distance between time series. This distance measure is defined by dynamic time warping method. The dimension of the distance matrix is very high. This paper introduces centroids of each class as a reference objects to decrease this dimension. The distance matrix with lower dimension describes the distance between all objects and reference objects. We use this method for human activity recognition and investigate the quality of classification on data from the mobile accelerometer. This metric algorithm of classification is compared with separating classification algorithm.
BibTeX:
 
@article{Goncharov2015MetricClassification, 
  author = {Goncharov, A. V. and Popova, M. S. and Strijov, V. V.},
  title = {Metric time series classification using dynamic warping relative to centroids of classes},
  journal = {Systems and Means of Informatics},
  year = {2015},
  volume = {25(4)},
  pages = {52-64},
  url = {http://strijov.com/papers/Goncharov2015MetricClassification.pdf}
}
Ignatov A., Strijov V. Human activity recognition using quasiperiodic time series collected from a single triaxial accelerometer // Multimedia Tools and Applications, 2015, 17.05.2015 : 1-14. Article
Abstract: The current generation of portable mobile devices incorporates various types of sensors that open up new areas for the analysis of human behavior. In this paper, we propose a method for human physical activity recognition using time series, collected from a single tri-axial accelerometer of a smartphone. Primarily, the method solves a problem of time series segmentation, assuming that each meaningful segment corresponds to one fundamental period of motion. To extract the fundamental period we construct the phase trajectory matrix, applying the technique of principal component analysis. The obtained segments refer to various types of human physical activity. To recognize these activities we use the k-nearest neighbor algorithm and neural network as an alternative. We verify the accuracy of the proposed algorithms by testing them on the WISDM dataset of labeled accelerometer time series from thirteen users. The results show that our method achieves high precision, ensuring nearly 96% recognition accuracy when using the bunch of segmentation and k-nearest neighbor algorithms.
BibTeX:
 
@article{Ignatov2015HumanActivity, 
  author = {Andrey Ignatov and Vadim Strijov},
  title = {Human activity recognition using quasiperiodic time series collected from a single triaxial accelerometer},
  journal = {Multimedia Tools and Applications},
  year = {2015},
  volume = {17.05.2015},
  pages = {1-14},
  url = {http://strijov.com/papers/Ignatov2015HumanActivity.pdf},
  doi = {10.1007/s11042-015-2643-0}
}
Katrutsa A.M., Kuznetsov M.P., Rudakov K.V., Strijov V.V. Metric concentration search procedure using reduced matrix of pairwise distances // Intelligent Data Analysis, 2015, 19(5) : 1091-1108. Article
Abstract: This paper presents a new fast clustering algorithm RhoNet, based on the metric concenration location procedure. To locate the metric concentration, the algorithm uses a reduced matrix of pairwise ranks distances. The key feature of the proposed algorithm is that it doesn't need the exhaustive matrix of pairwise distances. This feature reduces computational complexity. It is designed to solve the protein secondary structure recognition problem. The computational experiment collects tests and to hold performance analysis and analysis of dependency for the algorithm quality and structure parameters. The algorithm is compared with k-modes and tested on different metrics and data sets.
BibTeX:
 
@article{Katrutsa2014RhoNet, 
  author = {Katrutsa, A. M. and Kuznetsov, M. P. and Rudakov, K. V. and Strijov, V. V.},
  title = {Metric concentration search procedure using reduced matrix of pairwise distances},
  journal = {Intelligent Data Analysis},
  year = {2015},
  volume = {19(5)},
  pages = {1091-1108},
  url = {http://strijov.com/papers/Katrutsa2014RhoNetClustering.pdf},
  doi = {10.3233/IDA-150760}
}
Katrutsa A.M., Strijov V.V. The multicollinearity problem for feature selection methods in regression // Informational Technologies, 2015, 1 : 8-18. Article
Abstract: The paper investigates the multicollinearity problem in regression analysis and its influence on the performance of feature selection methods. The authors propose a procedure to test feature selection methods. A criteria is proposed to compare the feature selection methods, according to their performance when the multicollinearity is present. The feature selection methods are compared according to the other well-known evaluation measures. Methods to generate data sets of different multicollinearity types were proposed. The authors investigate performance of feature selection methods. The feature selection methods were tested on the data sets of different multicollinearity types.
BibTeX:
 
@article{Katrutsa2014TestGeneration, 
  author = {A. M. Katrutsa and V. V. Strijov},
  title = {The multicollinearity problem for feature selection methods in regression},
  journal = {Informational Technologies},
  year = {2015},
  volume = {1},
  pages = {8-18},
  url = {http://strijov.com/papers/Katrutsa2014TestGeneration.pdf}
}
Katrutsa A.M., Strijov V.V. Stresstest procedure for feature selection algorithms // Chemometrics and Intelligent Laboratory Systems, 2015, 142 : 172-183. Article
Abstract: This study investigates the multicollinearity problem and the performance of feature selection methods in case of datasets have multicollinear features. We propose a stresstest procedure for a set of feature selection methods. This procedure generates test data sets with various configurations of the target vector and features. A number of some multicollinear features are inserted in every configuration. A feature selection method results a set of selected features for given test data set. To compare given feature selection methods the procedure uses several quality measures. A criterion of the selected features redundancy is proposed. This criterion estimates number of multicollinear features among the selected ones. To detect multicollinearity it uses the eigensystem of the parameter covariance matrix. In computational experiments we consider the following illustrative methods: Lasso, ElasticNet, LARS, Ridge and Stepwise and determine the best one, which solve the multicollinearity problem for every considered configuration of dataset.
BibTeX:
 
@article{Katrutsa2015Stresstest, 
  author = {Katrutsa, A. M. and Strijov, V. V.},
  title = {Stresstest procedure for feature selection algorithms},
  journal = {Chemometrics and Intelligent Laboratory Systems},
  year = {2015},
  volume = {142},
  pages = {172-183},
  url = {http://strijov.com/papers/Katrutsa2014TestGenerationEn.pdf},
  doi = {10.1016/j.chemolab.2015.01.018}
}
Kuznetsov M.P., Clausel M., Amini M.-R., Gaussier E., Strijov V.V. Supervised topic classification for modeling a hierarchical conference structure // in S. Arik et al. (Eds.): International conference on neural information processing, Part 1, LNCS, 2015, 9489 : 90–97. Article
Abstract: In this paper we investigate the problem of supervised latent modelling for extracting topic hierarchies from data. The supervised part is given in the form of expert information over document-topic correspondence. To exploit the expert information we use a regularization term that penalizes the di erence between a predicted and an expertgiven model. We hence add the regularization term to the log-likelihood function and use a stochastic EM based algorithm for parameter estimation. The proposed method is used to construct a topic hierarchy over the proceedings of the European Conference on Operational Research and helps to automatize the abstract submission system.
BibTeX:
 
@article{TopicModelsICONIP2015, 
  author = {Kuznetsov, M. P. and Clausel, M. and Amini, M.-R. and Gaussier, E. and Strijov, V. V.},
  title = {Supervised topic classification for modeling a hierarchical conference structure},
  journal = {in S. Arik et al. (Eds.): International conference on neural information processing, Part 1, LNCS},
  year = {2015},
  volume = {9489},
  pages = {90–97},
  url = {http://strijov.com/papers/TopicModelsICONIP2015.pdf},
  doi = {10.1007/978-3-319-26532-2_11}
}
Motrenko A.P., Strijov V.V. Extracting fundamental periods to segment human motion time series // Journal of Biomedical and Health Informatics, 2015, 20(6) : 1466 - 1476. Article
Abstract: The paper addresses a problem of sensor-based time series segmentation as a part of human activity recognition problem. We assume that each studied time series contains a fundamenta periodic which can be seen as an ultimate entity (cycle) of motion. Due to the nature of the data and the urge to obtain interpretable results of segmentation, we defne the segmentation as a partition of the time series into the periods of this fundamental periodic. To split the time series into periods we select a pair of principal components of the Hankel matrix. We then cut the trajectory of the selected principal components by its symmetry axis, thus obtaining half-periods that are merged into segments. A method of selecting a pair of components, corresponding to the fundamental periodic is proposed.
BibTeX:
 
@article{Motrenko2015Fundamental, 
  author = {Motrenko, A. P. and Strijov, V. V.},
  title = {Extracting fundamental periods to segment human motion time series},
  journal = {Journal of Biomedical and Health Informatics},
  year = {2015},
  volume = {20(6)},
  pages = {1466 - 1476},
  url = {http://strijov.com/papers/MotrenkoStrijov2014RV2.pdf},
  doi = {10.1109/JBHI.2015.2466440}
}
Popova M.S., Strijov V.V. Selection of optimal physical activity classification model using measurements of accelerometer // Informatics and applications, 2015, 9(1) : 76-86. Article
Abstract: In this paper we solve the problem of selecting optimal stable models for classification of physical activity. Each type of physical activity of a particular person is described by a set of features generated from the accelerometer time series. In conditions of feature’s multicollinearity selection of stable models is hampered by the need to evaluate a large number of parameters of these models. Evaluation of optimal parameter values is also difficult due to the fact that the error function has a large number of local minima in the parameter space. In the paper we choose the optimal models from the class of two-layer artificial neural networks. We solve the problem of finding the Pareto optimal front of the set of models. The paper presents a stepwise strategy of building optimal stable models. The strategy includes steps of deleting and adding parameters, criteria of pruning and growing the model and criteria of breaking the process of building. The computational experiment compares models generated by the proposed strategy on three quality criteria~--- complexity, accuracy and stability.
BibTeX:
 
@article{Popova2014OptimalModelSelection, 
  author = {Maria S. Popova and Vadim V. Strijov},
  title = {Selection of optimal physical activity classification model using measurements of accelerometer},
  journal = {Informatics and applications},
  year = {2015},
  volume = {9(1)},
  pages = {76-86},
  url = {http://strijov.com/papers/Popova2014OptimalModelSelection.pdf}
}
Popova M.S., Strijov V.V. Building superposition of deep learning neural networks for solving the problem of time series classication // Systems and Means of Informatics, 2015, 25(3) : 60-77. Article
Abstract: This paper solves the problem of time-series classi cation using deep learning neural networks. The paper proposes to use a multilevel superposition of models belonging to the following classes of neural networks: two-layer neural networks, Boltzmann machines and autoencoders. Lower levels of superposition extract from noisy data of high dimensionality informative features, while the upper level of the superposition solves the problem of classi cation based on these extracted features. The proposed model has been tested on two samples of physical activity time series. The classi cation results obtained by proposed model in computational experiment were compared with the results which were obtained on the same datasets by foreign authors. The study showed the possibility of using deep learning neural networks for solving problems of time-series physical activity classi cation.
BibTeX:
 
@article{PopovaStrijov2015DeepLearning, 
  author = {Popova, M. S. and Strijov, V. V.},
  title = {Building superposition of deep learning neural networks for solving the problem of time series classication},
  journal = {Systems and Means of Informatics},
  year = {2015},
  volume = {25(3)},
  pages = {60-77},
  url = {http://strijov.com/papers/PopovaStrijov2015DeepLearning.pdf}
}
Rudakov K.V., Sanduleanu L.N., Tokmakova A.A., Yamschikov I.S., Reyer I.A., Strijov V.V. Terrain objects movement detection using SAR interferometry // Computer Research and Modeling, 2015, 7(5) : 1047-1060. Article
Abstract: To determine movements of infrastructure objects on Earth surface, SAR interferometry is used. The method is based on obtaining a series of detailed satellite images of the same Earth surface area at different times. Each image consists of the amplitude and phase components. To determine terrain movements the change of the phase component is used. A method of persistent scatterers detection and estimation of relative shift of objects corresponding to persistent scatterers is suggested.
BibTeX:
 
@article{Sanduleanu2016SAR, 
  author = {Rudakov, K. V. and Sanduleanu, L. N. and Tokmakova, A. A. and Yamschikov, I. S. and Reyer, I. A. and Strijov, V. V.},
  title = {Terrain objects movement detection using SAR interferometry},
  journal = {Computer Research and Modeling},
  year = {2015},
  volume = {7(5)},
  pages = {1047-1060},
  url = {http://strijov.com/papers/Rudakov_crm_2015.pdf},
  doi = {http://crm.ics.org.ru/journal/article/2370/}
}
Stenina M.M., Kuznetsov M.P., Strijov V.V. Ordinal classification using Pareto fronts // Expert Systems with Applications, 2015, 42(14) : 5947–5953. Article
Abstract: We solve an instance ranking problem using ordinal scaled expert estimations. The experts define a preference binary relation on the set of features. The instance ranking problem is considered as the monotone multiclass classification problem. To solve the problem we use a set of Pareto optimal fronts. The proposed method is illustrated with the problem of categorization of the IUCN Red List threatened species.
BibTeX:
 
@article{Medvednikova2014POF, 
  author = {Stenina, M. M. and Kuznetsov, M. P. and Strijov, V. V.},
  title = {Ordinal classification using Pareto fronts},
  journal = {Expert Systems with Applications},
  year = {2015},
  volume = {42(14)},
  pages = {5947–5953},
  url = {http://strijov.com/papers/Medvednikova2014POF.pdf},
  doi = {10.1016/j.eswa.2015.03.021}
}
Stenina M.M., Strijov V.V. Forecasts reconciliation for hierarchical time series forecasting problem // Informatics and applications, 2015, 9(2) : 77-89. Article
Abstract: The hierarchical time series forecasting problem is researched. Time series forecasts must satisfy the physical constraints and the hierarchical structure. In this paper a new algorithm for hierarchical time series forecasts reconciliation is proposed. The algorithm is called GTOp (Game-theoretically Optimal reconciliation). It guarantees that reconciled forecasts quality is not worse than self-dependent forecasts one. This approach is based on Nash equilibrium search for the antagonistic game and turn forecasts reconciliation problem into the optimization problem with equality and inequality constraints. It is proved that the Nash equilibrium in pure strategies exists in the game if some assumptions about the hierarchical structure, the physical constraints and the loss function are satisfied. The algorithm performance is demonstrated for different types of hierarchical structures of time series.
BibTeX:
 
@article{Stenina2014Reconciliation.pdf, 
  author = {Stenina, M. M. and Strijov, V. V.},
  title = {Forecasts reconciliation for hierarchical time series forecasting problem},
  journal = {Informatics and applications},
  year = {2015},
  volume = {9(2)},
  pages = {77-89},
  url = {http://strijov.com/papers/Stenina2014Reconciliation.pdf},
  doi = {10.14357/19922264150209}
}
Strijov V.V., Weber G.W., Weber R., Sureyya O.A. Editorial of the special issue data analysis and intelligent optimization with applications // Machine Learning, 2015, 101(1-3) : 1-4. Article
Abstract: This special issue on “Data Analysis and Intelligent Optimization with Applications” follows a previous special issue of this journal on the interplay of Machine Learning and Optimization, “Model Selection and Optimization in ML” (Machine Learning 85:1-2, October 2011). This time we shift our focus to applications of data analysis and optimization techniques. Optimization problems underlie most machine learning approaches. Due to emergence of new practical applications, new problems and challenges for traditional approaches arise. Emergent applications generate new data analysis problems, which, in turn boost new research in optimization. The contribution of machine learning researchers into the field of optimization is of considerable significance and should not be overlooked. This special issue collected solutions, adapted for real world problems, leading to massive and large-scale data sets, online data and imbalanced data. We encouraged submission of papers, devoted to combining machine learning and data analysis techniques with advances in optimization to produce methods of Intelligent Optimization, both theoretical and practical. Our goal for this special issue was to bring together researchers working in different areas, related to analytics and optimization.
BibTeX:
 
@article{Strijov2015Editorial, 
  author = {Strijov, V. V. and Weber, G. W. and Weber, R. and Sureyya, O. A.},
  title = {Editorial of the special issue data analysis and intelligent optimization with applications},
  journal = {Machine Learning},
  year = {2015},
  volume = {101(1-3)},
  pages = {1-4},
  url = {http://link.springer.com/content/pdf/10.1007%2Fs10994-015-5523-y.pdf},
  doi = {10.1007/s10994-015-5523-y}
}

2014

Aduenko A.A., Strijov V.V. Joint feature and object selection in multiclass classification of documents // Infocommunication Technologies, 2014, 1 : 47-54. Article
Abstract: The article is dedicated to the problem of search engine results ranking. The algorithm of multiclass classifi cation with joint selection of features and objects is proposed. It is modifi ed for interclass relevance comparison. Features and objects selection is performed with stepwise regression and with genetic algorithm. Results obtained using both algorithms are compared. Proposed multiclass classifi cation algorithm is tested on synthetic data and on data of Yandex search engine results.
BibTeX:
 
@article{Aduenko2013Multiclass, 
  author = {A. A. Aduenko and V. V. Strijov},
  title = {Joint feature and object selection in multiclass classification of documents},
  journal = {Infocommunication Technologies},
  year = {2014},
  volume = {1},
  pages = {47-54},
  url = {http://strijov.com/papers/Aduenko2013Multiclass.pdf}
}
Kuzmin A.A., Aduenko A.A., Strijov V.V. Thematic classification using expert model for major conference abstracts // Information Technologies, 2014, 6 : 22-26. Article
Abstract: The aim of this paper is to verify a thematic structure of the conference abstracts collection. The conference consists of main Areas; each main Area consists of Streams; each Stream contains Sessions; Session consists of several talks. This conference structure determines a thematic model of the conference. Thousands of scientists submit their abstracts and participate in the a major conference, and the its thematic model of such conference has a multilevel structure. The program committee constructs an expert thematic model of the conference every year. Due to the huge number of experts in program committee, they meet the problem of thematic integrity verification occurs. The aim of this paper is to find inconsistences in the expert thematic model using the a text clustering approach. We consider an abstracts collection with an given expert model. The base assumption is that the terms of the abstract determine the theme of this abstract and its position location in the thematic model. We propose the a similarity function of two abstracts and . The introduce a quality function, which determines the quality of the thematic model. It considering involves the intracluster and intercluster similarities. The proposed fast non-metric clustering algorithm maximizes the this quality function. To make the some constructed model similar with the given expert model, the algorithm modity doesn’t change a the constructed model if the increase of the quality function exceeds is less than a some set fixed value of the threshold parameter value. This threshold impacts on the number of revealed inconsistences in the expert model. The proposed method constructs a thematic model for the abstracts for EURO 2013.
BibTeX:
 
@article{Kuzmin2014Thematic, 
  author = {A. A. Kuzmin and A. A. Aduenko and V. V. Strijov},
  title = {Thematic classification using expert model for major conference abstracts},
  journal = {Information Technologies},
  year = {2014},
  volume = {6},
  pages = {22-26},
  url = {http://strijov.com/papers/Kuzmin2014Thematic.pdf}
}
Kuznetsov M.P., Strijov V.V. Methods of expert estimations concordance for integral quality estimation // Expert Systems with Applications, 2014, 41(4-2) : 1988-1996. Article
Abstract: The paper presents new methods of alternatives ranking using expert estimations and measured data. The methods use expert estimations of objects quality and criteria weights. This expert estimations are changed during the computation. The expert estimation are supposed to be measured in linear and ordinal scales. Each object is described by the set of linear, ordinal or nominal criteria. The constructed object estimations must not contradict both the measured criteria and the expert estimations. The paper presents methods of expert estimations concordance. The expert can correct result of this concordance.
BibTeX:
 
@article{KuznetsovStrijov2014MethodsExpert, 
  author = {M. P. Kuznetsov and V. V. Strijov},
  title = {Methods of expert estimations concordance for integral quality estimation},
  journal = {Expert Systems with Applications},
  year = {2014},
  volume = {41(4-2)},
  pages = {1988-1996},
  url = {http://strijov.com/papers/Kuznetsov-Strijov2013Concordance.pdf}
}
Motrenko A., Strijov V., Weber G.-W. Bayesian sample size estimation for logistic regression // Journal of Computational and Applied Mathematics, 2014, 255 : 743-752. Article
Abstract: The problem of sample size estimation is important in the medical applications, especially in the cases of expensive measurements of immune biomarkers. The papers describes the problem of logistic regression analysis including model feature selection and includes the sample size determination algorithms, namely methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regression model parameters as the multivariate variable, propose to estimate sample size using the distance between parameter distribution functions on cross-validated data sets.
BibTeX:
 
@article{Motrenko2013Bayesian, 
  author = {Anastasiya Motrenko and Vadim Strijov and Gerhard-Wilhelm Weber},
  title = {Bayesian sample size estimation for logistic regression},
  journal = {Journal of Computational and Applied Mathematics},
  year = {2014},
  volume = {255},
  pages = {743-752},
  url = {http://strijov.com/papers/MotrenkoStrijovWeber2012SampleSize.pdf},
  doi = {10.1016/j.cam.2013.06.031}
}
Motrenko A., Strijov V.V. Obtaining an aggregated forecast of railway freight transportation using Kullback–Leibler distance // Informatics and applications, 2014, 8(2) : 86-97. Article
Abstract: This study addresses the problem of obtaining an aggregated forecast of railway freight transportation. To improve the quality of aggregated forecast, we solve a time series clusterization problem, such that the time series in each cluster belong to the seme distribution. Solving the clusterization problem, we need to estimate the distance between empirical distributions of the time series. We introduce a two-sample test based on the Kullback-Leibler distance between histograms of the time series. We provide theoretical and experimental research of the suggested test. Also, as a demonstration, the clusterization of a set of railway time series based on the Kullback–Leibler distance between time series is obtained.
BibTeX:
 
@article{Motrenko2014KullbackLeibler, 
  author = {A.P. Motrenko and V. V. Strijov},
  title = {Obtaining an aggregated forecast of railway freight transportation using Kullback–Leibler distance},
  journal = {Informatics and applications},
  year = {2014},
  volume = {8(2)},
  pages = {86-97},
  url = {http://strijov.com/papers/MotrenkoStrijov2014KL.pdf}
}
Stenina M.M., Strijov V.V. Reconciliation of aggregated and disaggregated time series forecasts in nonparametric forecasting problems // Systems and Means of Informatics, 2014, 24(2) : 21-34. Article
Abstract: In many applications there are problems of forecasting a lot of time series with hierarchical structure. It is needed to reconcile forecasts across the hierarchy. In this paper new algorithm of reconciliation hierarchical time series forecasts is proposed. This algorithm is based on solving of optimization problem with constraints. Proposed algorithm allows to reconcile forecasts with nonplanar hierarchical structure and take into account physical constraints of forecasted values such as non-negativeness or maximal value. The algorithm performance is illustrated by railroad stations occupancy data in Omsk region. Forecasts quality is compared with forecasts quality optimal algorithm of reconciliation. Also the algorithm performance is demonstrated for nonplanar hierarchical structure of time series.
BibTeX:
 
@article{Stenina2014RailRoadsMatching, 
  author = {Stenina, M. M. and Strijov, V. V.},
  title = {Reconciliation of aggregated and disaggregated time series forecasts in nonparametric forecasting problems},
  journal = {Systems and Means of Informatics},
  year = {2014},
  volume = {24(2)},
  pages = {21-34},
  url = {http://strijov.com/papers/Stenina2014RailRoadsMatching.pdf}
}
Varfolomeeva A.A., Strijov V.V. An algorithm for bibliographic records parsing using structure learning methods // Information Technologies, 2014, 7 : 11-15. Article
Abstract: The paper solves the application problem of structured texts segmentation, namely each segment of a bibliographic record must correspond to its filed type of the BibTeX format and each record must correspond to its bibliographic type. This problem arises due to the existence of different standards for bibliographic records: an algorithm for determining the types of fields of bibliographic records, which is independent of the specific standards of their composition, should be proposed. To solve the problem of determining the field type the method of constructing matrix “objects” and matrices “answers” is proposed. The authors offer an algorithm of a bibliography lists parsing using the structure regression method, and the optimization problem of regression model’s parameters is also solved. According to the results of fields' segmentation bibliographic types of the records are clustered. The quality of the constructed model is investigated using a collection of non-parsed bibliography lists. In the paper it is shown the proposed algorithm has good quality of segmentation and clustering, if it has sufficient training sample.
BibTeX:
 
@article{VarfolomeevaStrijov2013FeatureSelection, 
  author = {Varfolomeeva, A. A. and Strijov, V. V.},
  title = {An algorithm for bibliographic records parsing using structure learning methods},
  journal = {Information Technologies},
  year = {2014},
  volume = {7},
  pages = {11-15},
  url = {http://strijov.com/papers/Varfolomeeva2013StrcLearning.pdf}
}
Aduenko A.A., Strijov V.V. Multimodelling and Object Selection for Banking Credit Scoring // Conference of the International Federation of Operational Research Societies, 2014 : 138. InProceedings
Abstract: To construct a bank credit scoring model one must select a set of informative objects (client records) to get the unbiased estimation of the model parameters. This set must have no outliers. The authors propose an object selection algorithm for mixture of regression models. It is based on analysis of the covariance matrix for the parameters estimations. The computational experiment shows statistical significance of the classification quality improvement. The algorithm is illustrated with the cash loans and heart disease data sets.
BibTeX:
 
@inproceedings{Aduenko2014MultomodelingMulticollinear_IFORS, 
  author = {Alexander A. Aduenko and Vadim V. Strijov},
  title = {Multimodelling and Object Selection for Banking Credit Scoring},
  booktitle = {Conference of the International Federation of Operational Research Societies},
  year = {2014},
  pages = {138},
  url = {http://strijov.com/papers/Aduenko2014MultiModel_IFORS.pdf}
}
Katrutsa A.M., Strijov V.V. Multicollinearity: Performance Analysis of Feature Selection Algorithms // Conference of the International Federation of Operational Research Societies, 2014 : 138. InProceedings
Abstract: We investigate the multicollinearity problem and its influence on the performance of feature selection methods. The paper proposes the testing procedure for feature selection methods. We discuss the criteria for comparing feature selection methods according to their performance when the multicollinearity is present. Feature selection methods are compared according to the other evaluation measures. We propose the method of generating test data sets with different kinds of multicollinearity. Authors conclude about the performance of feature selection methods if the multicollinearity is present.
BibTeX:
 
@inproceedings{Katrutsa2014MultomodelingMulticollinear_IFORS, 
  author = {Alexandr M. Katrutsa and Vadim V. Strijov},
  title = {Multicollinearity: Performance Analysis of Feature Selection Algorithms},
  booktitle = {Conference of the International Federation of Operational Research Societies},
  year = {2014},
  pages = {138},
  url = {http://strijov.com/papers/Katrutsa2014MultiCollinear_IFORS.pdf}
}
Kuzmin A.A., Aduenko A.A., Strijov V.V. Thematic Classification for EURO/IFORS Conference Using Expert Model // Conference of the International Federation of Operational Research Societies, 2014 : 175. InProceedings
Abstract: The decision support system predicts the areas, streams and sessions for the abstracts of a major conference. Abstract collections from the previous EURO/IFORS (2010, 2012, 2013) conferences and their expert thematic models are considered. The terminological dictionary of the conference and the global thematic model of these collections are constructed. A similarity function between two abstracts is proposed. The non-metric hierarchical clustering algorithm which considers a constructed global thematic model is used to construct the thematic model of a new conference without an expert model.
BibTeX:
 
@inproceedings{Kuzmin2014Thematic_INFORS, 
  author = {Arsentii A. Kuzmin and Alexander A. Aduenko and Vadim V. Strijov},
  title = {Thematic Classification for EURO/IFORS Conference Using Expert Model},
  booktitle = {Conference of the International Federation of Operational Research Societies},
  year = {2014},
  pages = {175},
  url = {http://strijov.com/papers/Kuzmin2014Thematic_INFORS.pdf}
}
Kuznetsov M.P., Strijov V.V. Partial Orders Combining for the Object Ranking Problem // Conference of the International Federation of Operational Research Societies, 2014 : 157. InProceedings
Abstract: We propose a new method for the ordinal-scaled object ranking problem. The method is based on the combining of partial orders corresponding to the ordinal features. Every partial order is described with a positive cone in the object space. We construct the solution of the object ranking problem as the projection to a superposition of the cones. To restrict model complexity and prevent overfitting we reduce dimension of the superposition and select most informative features. The proposed method is illustrated with the problem of the IUCN Red List monotonic categorization.
BibTeX:
 
@inproceedings{Kuznetsov2014PartialOrders_IFORS, 
  author = {Mikhail P. Kuznetsov and Vadim V. Strijov},
  title = {Partial Orders Combining for the Object Ranking Problem},
  booktitle = {Conference of the International Federation of Operational Research Societies},
  year = {2014},
  pages = {157},
  url = {http://strijov.com/papers/Kuznetsov2014PartialOrder_IFORS.pdf}
}
Matrosov M., Strijov V.V. Short-Term Forecasting of Musical Compositions Using Chord Sequences // Conference of the International Federation of Operational Research Societies, 2014 : 229. InProceedings
Abstract: The objective is to predict a sequence of chords. It is treated as multivariate time series of discrete values. A chord is represented as an array of half-tone sounds within one octave. We utilize a classifier based on probability distributions over chord sequences that are estimated both on a big training set and some revealed part of the forecasted melody. It shows robust forecasting on a set of 50 000 midi files. The novelty is model selection algorithm and invariant representation of chords. The same technique can be used to predict or synthesize various types of discrete time series.
BibTeX:
 
@inproceedings{Matrosov2014Musical_IFORS, 
  author = {Mikhail Matrosov and Vadim V. Strijov},
  title = {Short-Term Forecasting of Musical Compositions Using Chord Sequences},
  booktitle = {Conference of the International Federation of Operational Research Societies},
  year = {2014},
  pages = {229},
  url = {http://strijov.com/papers/Matrosov2014Musical_IFORS.pdf}
}
Strijov V.V., Kuznetsov M.P., Motrenko A.P. Structure learning and forecasting model generation // Conference of the International Federation of Operational Research Societies, 2014 : 101. InProceedings
Abstract: The aim of the study is to suggest a method to forecast a structure of a regression model superposition, which approximates a data set in terms of some quality function. The problem: algorithms of model selection are computationally complex due to the large number of models. The solution: we developed a model structure forecasting algorithm based on previously selected models.
BibTeX:
 
@inproceedings{Strijov2014Structure_IFORS, 
  author = {V. V. Strijov and M. P. Kuznetsov and A. P. Motrenko},
  title = {Structure learning and forecasting model generation},
  booktitle = {Conference of the International Federation of Operational Research Societies},
  year = {2014},
  pages = {101},
  url = {http://strijov.com/papers/Strijov2014StructLearning_IFORS.pdf}
}
Sologub R.A. Algorithms of inductive model generation and transformation for non-linear regression problems (PhD thesis supervised by V.V. Strijov). Russian Academy of Sciences, Computing Center, 2014. PhdThesis
Abstract: The thesis provides a solution for the problem of automatic generation and validation the quantitative mathematical models. The considered models are used for describing the results of measurements and experiments. In the thesis we investigate a fundamental problem of automatic model generation for in the data analysis field. The generated models are used for approximation, analysis and forecasting the results of experiments. To generate a model we consider the expert-given requirements on the model structure. This consideration allows us to construct the interpretable models that adequately describe the results of measurements. To construct an adequate model we use expert-given basic functions and a set of generation rules. The model is represented as a superposition of the basic functions. The generation rules define the admissibility of superpositions and exclude the generation of isomorphic models. We propose to develop the existing methods of automatic model generation. In particular, we propose to consider expert requirements to the model structure and to rank the models according to the expert preferences. The proposed methods of the isomorphic superpositions search are based on the isomorphic subgraphs search and on the substitution of graphs. We investigate the methods and algorithms of model generation, their properties, complexity and stability. While solving an applied problem of mathematical modeling, the existing knowledges and expert information about model structure are often insufficient to construct the efficient model. Lack of the independent variables makes the methods of model and feature generation very perspective. The idea of feature generation based on the generation of the new independent variables - images of the original variables over the set of successive mappings. This mappings are called the basic functions. Previously the applied problems were considered in terms of the present approach. The basic functions construction and feature generation approaches were used for the economic and industrial problems. While solving this problems, the researchers didn’t investigate the existence, completeness and correctness of the proposed algorithm. In the thesis we develop the theoretical validation of correctness and admissibility of the superpositions generation methods and the methods convergence. We propose methods of optimization of the model structure. The group method of data handling, an example of the model generation method, was considered by A.G. Ivakhnenko. In the case of linear model the method generates new features using the multiplication operation. Using the Kolmogorov-Gabor polynomials, the algorithm generates the models of different complexity by the set of criteria. As a result, the method finds the model of optimal complexity described by an equation or a system of equations. An important stage of development of regression models was a consideration of non-linear models. This approach is widely described by G. Seber: he considered construction and parameter estimation for the non-linear models. To estimate the parameters, there was propose a Levenberg-Marquardt method. J. Koza and N. Zelinka proposed a symbolic regression technique for inductive model generation. The method found an optimal model from the set of superpositions by the genetic programming. The inductive model generation was used to solve an applied problem of the optimal antenna form determination. V.V. Strijov developed the ideas of the inductive model generation by using the coherent Bayesian inference for the parameter estimation. While analysing the model structure, the most convenient way of the superposition representation is a graph-tree. Thereby the methods of graph transformation are applied to the superpositions. This methods allow us to describe formally the structure optimization procedures. We consider categorial representation of graph transformations and conditions of the rules usability. For the trees transformation we use the elementary patterns of graphs and construct the isomorphic graphs of the more complex structure.
BibTeX:
 
@phdthesis{Sologub2014PhDThesis, 
  author = {Sologub, R. A.},
  title = {Algorithms of inductive model generation and transformation for non-linear regression problems (PhD thesis supervised by V.V. Strijov)},
  school = {Russian Academy of Sciences, Computing Center},
  year = {2014},
  url = {http://strijov.com/papers/Sologub2014Disser-0018d.pdf}
}
Strijov V.V. Model genetation and selection for regression and classification problems (DSc Thesis). Russian Academy of Sciences, Computing Center, 2014. PhdThesis
Abstract: The thesis is devoted to the problem of model selection for regression and classification. According to the proposed approach, the models are selected from the inductively generated set. We propose to analyse the distribution of model parameters to choose the model of optimal complexity. There are two ways to construct the models, describing an observed data: mathematical modelling and data analysis. Models of the first type can be interpreted by the experts in the field of study [Krasnoshchyokov: 2000]. Models of the second type perform more efficiently, but don’t always have a clear interpretation [Bishop: 2006]. An actual problem of theoretical computer science is to combine advantages of the two approaches to obtain efficient interpretable models. The key issue is to construct the adequate regression and classification models for the forecasting problems. The problem is to find the models of optimal complexity describing the data with given accuracy. An additional restriction is an interpretability of the models for the expert in the field of study. The goal of research is to propose and investigate methods of model selection from the inductively generated set. The problem of model selection from the countable successively generated set is novel. To formulate the problem setting we used the broad material in the fields of model and feature selection, that is one of the key problems in the machine learning and data analysis area. The basic problem of study is to develop the methods of the successive models generation and of the parameters distribution estimation. The estimations of parameter covariance matrices are used for simplification the model selection procedure. The key challenge of this problem is the parameters estimation of the big number of structurally complex regression models. Relation between model generation and selection problems was investigated by A.G. Ivakhnenko in the early 1980s. According to the proposed group method of data handling [Ivakhnenko: 1981, Madala: 1994], the model of optimal structure can be found by the successive generation of linear models using the Kolmogorov-Gabor polynomial of the independent variables. The criteria of optimal model structure is given by the cross validation procedure. Unlike the GMDH, the symbolic regression method [Koza: 2005, Zelinka: 2008] generates arbitrary non-linear superpositions of basic functions. In the last years the problem of model complexity analysis for symbolic regression became significant field of study [Hazan: 2006, Vladislavleva: 2009]. Initially the methods of inductive model generation were proposed in terms of the group method of data handling. The structure of superposition was defined by the external quality criteria. Afterwards this criteria were explained in terms of data generation hypothesis and the Bayesian inference. To solve a problem of successive model generation, there arises a problem of estimation of the superposition elements informativity. In terms of the Bayesian regression [Bishop: 2000], to estimate informativity the probability density of model parameters is used. The probability density is a parametric function; its parameters referred to as hyperparameters [Bishop : 2006]. The hyperparameters analysis can be regarded as one of model selection methods. For the modification of the non-linear models superposition there was proposed an optimal brain damage method [LeCun: 1990]. According to this method, an element of the superposition is regarded as non-informative, if the saliency value of an error function doesn’t exceed the given threshold. The model selection problem is one of the key problems of the regression analysis field. One of the present model selection methods is the minimum description length principle. The MDL principle chooses the best compressed efficient model [Grunwald: 2005]. The problem of models comparison is investigated in detail by [MacKay: 1994-2003]. As an alternative to the information criteria [Burnham: 2002, Lehman: 2005], there was proposed a coherent Bayesian inference. The first level estimates the model parameters. The second level makes the hyperparameters adjusting. According to this method, the chance to select more complex model, at the comparable values of the error function, is less. The principles of the Bayesian approach in the linear model case were proposed by the authors [Celeux: 2006, Massart: 2008, Fleury: 2006]. At the same time, the mentioned principles and approaches remain open the questions investigated in the present thesis. By this reason we propose to create and develop the theory of regression model generation and selection. The problem is as follows. The set of models of the given class is inductively generated by the set of parametric basic functions given by the experts. Each model is an admissible superposition of the basic functions. The models interpretability is guaranteed by the expert-given basic functions, that are the basic elements of the model superposition. Each class of models is defined by the rules of superposition generation. The required model accuracy achieved by the consideration of the wideness of the basic models class. The optimum criteria includes the concepts of model complexity and accuracy, as well as the data generation hypothesis. Along with the parameter estimations, the proposed method estimates the model hyperparameters. Using information about the hyperparameters, the method estimates informativity of the superposition elements and optimizes the superposition structure. The optimum criteria, given by the data generation hypothesis, allows to choose the optimal models. Thus, we propose a new approach to the formulated problem. The set of models is generated inductively from the set of basic functions given by the experts. Each model is considered as the admissible superposition of the basic functions. Together with the parameters estimation we propose to estimate the hyperparameters of the parameters distribution. Using the parameter estimations we measure the informativity of the superposition elements and optimize the model structure. We choose the optimal model according to the quality criteria given by the data generation hypothesis. Сonstruction of the new methods of model selection for the classification and regression is a major and actual problem of the recognition theory.
BibTeX:
 
@phdthesis{Strijov2014DScThesis, 
  author = {Strijov, V. V.},
  title = {Model genetation and selection for regression and classification problems (DSc Thesis)},
  school = {Russian Academy of Sciences, Computing Center},
  year = {2014},
  url = {http://strijov.com/papers/Strijov2015ModelSelectionRu.pdf}
}

2013

Aduenko A.A., Strijov V.V. Optimal text placement for titles of documents in collection // Software Engineering, 2013, 3 : 21-25. Article
Abstract: Consider the method of visualization of the results of thematic clustering of documents’ collection. Pairwise-distance matrix is projected on plain using PCA. It is required to place the titles of dociments on plain. The loss function, which allows to reach a minimal overlap, is suggested. For its optimisation BFGS algorithm is used. Method suggested in the article is illustrated by visualization of conference’s thesis.
BibTeX:
 
@article{Aduenko2013TextVisualizing, 
  author = {A. A. Aduenko and V. V. Strijov},
  title = {Optimal text placement for titles of documents in collection},
  journal = {Software Engineering},
  year = {2013},
  volume = {3},
  pages = {21-25},
  url = {http://strijov.com/papers/AduenkoStrijov2013TextVisualizing.pdf}
}
Budnikov E.A., Strijov V.V. Estimating probabilities of text strings in document collections // Information Technologies, 2013, 4 : 40-45. Article
Abstract: Consider the problem of estimating the probabilities of strings in a document. To solve the problem, the model of n-grams is used. The n-gram classes is proposed to solve the estimation problem the large number of model parameters. Three discount models: Good-Turing, Katz and absolute discounting are used to solve the problem of zero probability of strings. The proposed model is illustrated by computational experiments on real data.
BibTeX:
 
@article{BudnikovStrijov2013Estimation, 
  author = {E. A. Budnikov and V. V. Strijov},
  title = {Estimating probabilities of text strings in document collections},
  journal = {Information Technologies},
  year = {2013},
  volume = {4},
  pages = {40-45},
  url = {http://strijov.com/papers/BudnikovStrijov2013Estimation.pdf}
}
Ivanova A.V., Aduenko A.A., Strijov V.V. Algorithm of construction logical rules for text segmentation // Software Engineering, 2013, 6 : 41-48. Article
Abstract: Consider the method of recovery of BibTeX-structure bibliographic records from their text representation. Structure is recovered using logical rules defined on an expert-given set of regular expressions. Algorithm based on stub covers is proposed for constructing the logic rules. The algorithm is illustrated with the problem of searching the structure in bibliographic records, represented by text strings.
BibTeX:
 
@article{IvanovaAduenkoStrijov2013TextMarkUp, 
  author = {A. V. Ivanova and A. A. Aduenko and V. V. Strijov},
  title = {Algorithm of construction logical rules for text segmentation},
  journal = {Software Engineering},
  year = {2013},
  volume = {6},
  pages = {41-48},
  url = {http://strijov.com/papers/Ivanova2012LogicStructureCor.pdf}
}
Kuzmin A.A., Strijov V.V. Validation of thematic models for document collections // Software Engineering, 2013, 4 : 16-20. Article
Abstract: Consider a collection of documents with expert thematic model. To verify the adequacy of the expert model build an algorithmic model by hierarchical clustering text collections. The agglomerative and divisive clustering methods are investigated. The algorithmic model error in comparison to the expert model is estimated. The differences between expert model and algorithmic model are visualized.
BibTeX:
 
@article{Kuzmin2013ThematicClustering, 
  author = {A. A. Kuzmin and V. V. Strijov},
  title = {Validation of thematic models for document collections},
  journal = {Software Engineering},
  year = {2013},
  volume = {4},
  pages = {16-20},
  url = {http://strijov.com/papers/Kuzmin2013ThematicClustering.pdf}
}
Medvednikova M.M., Strijov V.V. Construction of rank-scaled quality integral indicator for scientific publications in using co-clustering // Notices of Tula State University, 2013, 1 : 154-165. Article
Abstract: The method of the scientific publications quality measurement is proposed. It connects the quality of researcher’s publication and the quality of a journal in which the researcher publishes his article. The joined integral indicator is computed for the list of previous years publications using the collaborative filtering algorithm. A proximity function of authors and journals’ integral indicators is proposed as the quality functional. The involvement of the researchers’ and publishers’ integration into the international science is estimated.
BibTeX:
 
@article{Medvednikova2013CoIndicator, 
  author = {M. M. Medvednikova and V. V. Strijov},
  title = {Construction of rank-scaled quality integral indicator for scientific publications in using co-clustering},
  journal = {Notices of Tula State University},
  year = {2013},
  volume = {1},
  pages = {154-165},
  url = {http://strijov.com/papers/Medvednikova2012CoIndicator.pdf}
}
Rudoy G.I., Strijov V.V. Algorithms for inductive generation of superpositions for approximation of experimental data // Informatics and applications, 2013, 7(1) : 17-26. Article
Abstract: The paper presents an algorithm which inductively generates admissible non-linear models. An algorithm to generate all admissible superpositions of given complexity in finite number of iterations is proposed. The proof of its correctness is stated. The proposed approach is illustrated by a computational experiment on synthetic data.
BibTeX:
 
@article{Rudoy2013Generation, 
  author = {Rudoy, Georgiy I. and Strijov, Vadim V.},
  title = {Algorithms for inductive generation of superpositions for approximation of experimental data},
  journal = {Informatics and applications},
  year = {2013},
  volume = {7(1)},
  pages = {17-26},
  url = {http://strijov.com/papers/Rudoy2012Generation_Preprint.pdf}
}
Strijov V.V. Error function in regression analysis // Factory Laboratory, 2013, 79(5) : 65-73. Article
Abstract: В работе описаны методы назначения функции ошибки при постановке задач регрессионного анализа. Рассматриваются различные гипотезы распределения зависимой переменной, задающие вид функции ошибки согласно байесовскому выводу. Для нормального распределения показана функция ошибки общего вида для различных предположений о статистической связи между элементами зависимой переменной. Также приведены примеры функций ошибок, используемых в прикладных задачах восстановления регрессии.
BibTeX:
 
@article{Strijov2013ErrorFunction, 
  author = {Strijov, V. V.},
  title = {Error function in regression analysis},
  journal = {Factory Laboratory},
  year = {2013},
  volume = {79(5)},
  pages = {65-73},
  url = {http://strijov.com/papers/Strijov2012ErrorFn.pdf}
}
Strijov V.V., Krymova E.A., Weber G.W. Evidence optimization for consequently generated models // Mathematical and Computer Modelling, 2013, 57(1-2) : 50-56. Article
Abstract: To construct an adequate regression model one has to fulfill the set of measured features with their generated derivatives. Often the number of these features exceeds the number of the samples in the data set. After a feature generation process the problem of feature selection from a set of highly correlated features arises. The proposed algorithm uses an evidence maximization procedure to select a model as a subset of generated features. During the selection process it rejects multicollinear features. A problem of European option volatility modeling illustrates the algorithm. Its performance is compared with the performances of similar well-known algorithms.
BibTeX:
 
@article{Strijov11Evidence, 
  author = {Strijov, V. V. and Krymova, E. A. and Weber, G. W.},
  title = {Evidence optimization for consequently generated models},
  journal = {Mathematical and Computer Modelling},
  year = {2013},
  volume = {57(1-2)},
  pages = {50-56},
  url = {http://www.sciencedirect.com/science/article/pii/S0895717711001075},
  doi = {10.1016/j.mcm.2011.02.017}
}
Tsyganova S.V., Strijov V.V. The construction of hierarchical thematic models for document collection // Applied Informatics, 2013, 1 : 109-115. Article
Abstract: This work is devoted to detection themes of document collection and to their hierarchical structure. The main task is to construct hierarchical thematic model for documents' collection. To solve this task it's suggested to use probabilistic topic models. The main attention is paid to hierarchical thematic models and, particulary, to discuss the properties of PLSA and LDA algorythms. The peculiarity of construction of hierarchical model is the crossing from the conception of "bag of words" to conception of "bag of themes". The work is illustrate on theses of EURO-2012 conference and on synthetic data.
BibTeX:
 
@article{TsyganovaStrijov2013Hierarchical, 
  author = {Tsyganova, S. V. and Strijov, V. V.},
  title = {The construction of hierarchical thematic models for document collection},
  journal = {Applied Informatics},
  year = {2013},
  volume = {1},
  pages = {109-115},
  url = {http://strijov.com/papers/Tsyganova2013TopicHierarchy.pdf}
}
Zaytsev A.A., Strijov V.V., Tokmakova A.A. Estimation regression model hyperparameters using maximum likelihood // Informational Technologies, 2013, 2 : 11-15. Article
Abstract: The papers considers the regression model selection problem. The model parameters are supposed to be a multivariate random variable with independently distributed components. A method for hyperparameters optimization is proposed. Direct way to obtain the hyperparameters estimations is shown. The papers illustrated the usage of the hyperparameters in the feature selection problem. The suggested method is compared with the Laplace approximation method.
BibTeX:
 
@article{Zaitsev2012Estimation, 
  author = {A. A. Zaytsev and V. V. Strijov and A. A. Tokmakova},
  title = {Estimation regression model hyperparameters using maximum likelihood},
  journal = {Informational Technologies},
  year = {2013},
  volume = {2},
  pages = {11-15},
  url = {http://strijov.com/papers/ZaytsevStrijovTokmakova2012Likelihood_Preprint.pdf}
}
Aduenko A.A., Kuzmin A.A., Strijov V.V. Hierarchical thematic model visualizing algorithm // 26th European Conference on Operational Research, 2013 : 155. InProceedings
Abstract: The talk is devoted to the problem of the thematic hierarchical model construction. One must to construct a hierarchcal model of a scientific conference abstracts, to check the adequacy of the expert models and to visualize hierarchical differences between the algorithmic and expert models. An algorithms of hierarchical thematic model constructing is developed. It uses the notion of terminology similarity to construct the model. The obtained model is visualized as the plane graph.
BibTeX:
 
@inproceedings{KuzminStrijov2013VisualizingEURO, 
  author = {Aduenko, A. A. and Kuzmin, A. A. and Strijov, V. V.},
  title = {Hierarchical thematic model visualizing algorithm},
  booktitle = {26th European Conference on Operational Research},
  year = {2013},
  pages = {155}
}
Kuznetsov M.P., Strijov V.V. The IUCN Red List threatened speices categorization algorithm // 26th European Conference on Operational Research, 2013 : 352. InProceedings
Abstract: The main purpose of the IUCN Red List is to categorize those plants and animals that are facing a high risk of extinction. Species are classified by the IUCN Red List into nine groups ordered by the the relative risk of extinction in the wild nature. Each species is described with the rank-scaled features given by the experts. The problem is to associate each species with one of the groups according to the data given by the experts. We consider the rank-scaled features as the cones in the space of objects and construct the solution as the nearest point to the superposition of this cones.
BibTeX:
 
@inproceedings{KuznetsovStrijov2013RedListEURO, 
  author = {Kuznetsov, M. P. and Strijov, V. V.},
  title = {The IUCN Red List threatened speices categorization algorithm},
  booktitle = {26th European Conference on Operational Research},
  year = {2013},
  pages = {352}
}
Strijov V.V. Credit Scorecard Development: Model Generation and Multimodel Selection // 26th European Conference on Operational Research, 2013 : 220. InProceedings
Abstract: The talk is devoted to the automatic model generation for application scoring. According to the bank requirements a scorecard consists of a combination of the logistic regression models. We will discuss the following problems: First, how many models we must generate? Second, which model from the generated model set should be used to compute the probability of default for a newcomer client? Third, what features must be selected for the models? These problems must be resolved to develop a precise, stable and simple scorecard.
BibTeX:
 
@inproceedings{Strijov2013ScorecardEURO, 
  author = {Strijov, V. V.},
  title = {Credit Scorecard Development: Model Generation and Multimodel Selection},
  booktitle = {26th European Conference on Operational Research},
  year = {2013},
  pages = {220},
  url = {http://strijov.com/papers/Strijov2013EUROscoring.pdf}
}

2012

Aduenko A.A., Kuzmin A.A., Strijov V.V. Feature selection and metrics optimisation for document collection clustering // Notices of Tula State University, 2012, 3 : 119-131. Article
Abstract: This paper deals with the problem of verification of correctness of a thematic clustering of texts with the help of metric algorithms. The algorithm of selection the optimal distance function for texts is proposed. Correspondence between received texts’ clustering and their expert classification is studied. The results of clusterisation and their correspondence to expert thematic classification are illustrated in the computing experiment on the real text collection.
BibTeX:
 
@article{AduenkoKuzminStrijov2013Selection, 
  author = {A. A. Aduenko and A. A. Kuzmin and V. V. Strijov},
  title = {Feature selection and metrics optimisation for document collection clustering},
  journal = {Notices of Tula State University},
  year = {2012},
  volume = {3},
  pages = {119-131},
  url = {http://strijov.com/papers/Kuzmin2013ThematicClustering.pdf}
}
Kuznetsov M.P., Strijov V.V., Medvednikova M.M. Multiclass classification of objects with the rank-scale description // Notices on Science and Technology of SPb. PSU, 2012, 5 : 92-95. Article
Abstract: The authors propose a method of an integral indicator construction based on the rank-scaled description matrix given by an expert. The authors propose three-step iterative algorithm to estimate correction parameters and features weights. The feature selection problem is investigated. The method illustrated with the problem of classification of the Red Book of Russian Federation rare species statuses.
BibTeX:
 
@article{Kuznetsov2012RankScales, 
  author = {Kuznetsov, M. P and Strijov, V. V. and Medvednikova, M. M.},
  title = {Multiclass classification of objects with the rank-scale description},
  journal = {Notices on Science and Technology of SPb. PSU},
  year = {2012},
  volume = {5},
  pages = {92-95},
  url = {http://strijov.com/papers/Kuznetsov2012Curvilinear.pdf}
}
Medvednikova M.M., Strijov V.V., Kuznetsov M.P. Algorithm of multiclass monotonous Pareto-classification // Notices of Tula State University, 2012, 3 : 132-141. Article
Abstract: The authors propose a method to search a monotonous function, which is defined on the cartesian product of the linearly-ordered sets. The method is based on the procedures of monotonization of the discrete-argument function and Pareto-optimal front slicing. The feature selection problem investigated. The problem illustrated with the problem of forecasting of the Red Book of Russian Federation rare-spices statuses.
BibTeX:
 
@article{Medvednikova2012RankScales, 
  author = {Medvednikova, Mariya M. and Strijov, Vadim V. and Kuznetsov, Mikhail P.},
  title = {Algorithm of multiclass monotonous Pareto-classification},
  journal = {Notices of Tula State University},
  year = {2012},
  volume = {3},
  pages = {132-141},
  url = {http://strijov.com/papers/Medvednikova2012RankScales.pdf}
}
Motrenko A.P., Strijov V.V. Multiclass logistic regression for cardio-vascular disease forecasting // Notices of Tula State University, 2012, 1 : 153-162. Article
Abstract: The paper describes an algorithm to classify four groups of patients: a cardio-vascular disease group, a cardio-risk group and two types of healthy groups. The blood-cells protein measurements are the description features for an investigated patient. The paper develops an algorithm to forecast a patient’s cardio-vascular disease case as one of four unordered classes. The problem is to estimate the regression parameters and select the most informative features for multi-class classification. During the forecasting all pairs of the classes are considered.
BibTeX:
 
@article{Motrenko2012CVD, 
  author = {A. P. Motrenko and V. V. Strijov},
  title = {Multiclass logistic regression for cardio-vascular disease forecasting},
  journal = {Notices of Tula State University},
  year = {2012},
  volume = {1},
  pages = {153-162},
  url = {http://strijov.com/papers/MotrenkoStrijov2012HAPrediction.pdf}
}
Sanduleanu L.N., Strijov V.V. Feature selection for autoregressive forecasting // Informational Technologies, 2012, 6 : 11-15. Article
Abstract: The authors investigate the optimal model selection problem with application to the auto-regression forecasting. To solve the problem one has to select a maximum well-defined feature subset, subject to some given value of the error function. To select the feature set the modified add-del feature selection algorithm is used. This paper suggests a method of time series forecasting model selection. The computational experiment compares the electricity hourly prices forecasts.
BibTeX:
 
@article{Sanduleanu2012FeatureSelection_IT, 
  author = {L. N. Sanduleanu and V. V. Strijov},
  title = {Feature selection for autoregressive forecasting},
  journal = {Informational Technologies},
  year = {2012},
  volume = {6},
  pages = {11-15},
  url = {http://strijov.com/papers/SanduleanuStrijov2011FeatureSelection_Preprint.pdf}
}
Strijov V.V., Kuznetsov M.P., Rudakov K.V. Rank-scaled metric clustering of amino-acid sequences // Mathematical Biology and Bioinformatics, 2012, 7(1) : 345-359. Article
Abstract: To solve the problem of the secondary protein structure recognition, an algorithm for amino-acid subsequences clustering is developed. To reviel clusters it uses the pairwise distances between the subsequences. The algorithm does not require the complete pairwise matrix. This main distinction of it implies the reduction of the computational complexity. To run the clustering, it needs no more than the ranks of the distances between subsequences. The algorithm is illustrated using synthetic data along with the amino-acid sequences from the UniProt database.
BibTeX:
 
@article{Strijov2012Clustering, 
  author = {Strijov, V. V. and Kuznetsov, M. P. and Rudakov, K. V.},
  title = {Rank-scaled metric clustering of amino-acid sequences},
  journal = {Mathematical Biology and Bioinformatics},
  year = {2012},
  volume = {7(1)},
  pages = {345-359},
  url = {http://strijov.com/papers/Strijov2012(7_345).pdf}
}
Tokmakova A.A., Strijov V.V. Estimation of linear model hyperparameters for noisy or correlated feature selection problem // Informatics and applications, 2012, 6(4) : 66-75. Article
Abstract: This paper deals with the problem of feature selection in linear regression models. To select features authors estimate the covariance matrix of the model parameters. Dependent variable and model parameters are assumed to be normally distributed vectors. Laplace approximation is used for estimation of the covariance matrix: logarithm of the error function is approximated by the normal distribution function. The problem of noise or correlated features is also examined, since in this case the model parameters covariance matrix becomes singular. An algorithm for feature selection is proposed. The results of the study for a time series are given in the computational experiment.
BibTeX:
 
@article{Tokmakova2012Hyper, 
  author = {A. A. Tokmakova and V. V. Strijov},
  title = {Estimation of linear model hyperparameters for noisy or correlated feature selection problem},
  journal = {Informatics and applications},
  year = {2012},
  volume = {6(4)},
  pages = {66-75},
  url = {http://strijov.com/papers/Tokmakova2011HyperParJournal_Preprint.pdf}
}
Kuznetsov M.P., Strijov V.V. Integral indicator construction using rank-scaled design matrix // Intellectual Information Processing. Conference proceedings, 2012 : 130-132. InProceedings
Abstract: Описан способ построения интегральных индикаторов качества объектов с использованием экспертных оценок и измеряемых данных. Каждый объект описан набором признаков в ранговых шкалах. Используется экспертная оценка качества объектов, которая корректируется в процессе вычисления. Эта оценка выставлена в линейной шкале. Рассматривается задача получения таких интегральных индикаторов, которыене противоречили бы экспертной оценке. Для этого по матрице описаний строится множество значений интегрального индикатора. Интегральный индикатор определяется проекцией экспертной оценки на это множество.
BibTeX:
 
@inproceedings{Kuznetsov2012IOI, 
  author = {Kuznetsov, M. P. and Strijov, V. V.},
  title = {Integral indicator construction using rank-scaled design matrix},
  booktitle = {Intellectual Information Processing. Conference proceedings},
  year = {2012},
  pages = {130--132},
  url = {http://strijov.com/papers/Kuznetsov2012IOI.pdf}
}
Motrenko A., Strijov V., Weber G.-W. Bayesian sample size estimation for logistic regression // International Conference on Applied and Computational Mathematics, 2012 : 1-5. InProceedings
Abstract: The paper is devoted to the logistic regression analysis, applied to classification problems in biomedicine. A group of patients is investigated as a sample set; each patient is described with a set of features, named as biomarkers and is classified into two classes. Since the patient measurement is expensive the problem is to reduce number of measured features in order to increase sample size. The responsive variable is assumed to follow a Bernoulli distribution. Also, parameters of the regression function are evaluated. With given set of features, the model is excessively complex. The problem is to select a set of features of smaller size, that will classify patients effectively. In logistic regression features are usually selected by stepwise regression. In the computational experiment, exhaustive search is implemented. This makes the experts sure that all possible combinations of the features were considered. The authors use the area under ROC curve as the optimum criterion in the feature selection procedure.
BibTeX:
 
@inproceedings{Motrenko2012Bayesian, 
  author = {Anastasiya Motrenko and Vadim Strijov and Gerhard-Wilhelm Weber},
  title = {Bayesian sample size estimation for logistic regression},
  booktitle = {International Conference on Applied and Computational Mathematics},
  year = {2012},
  pages = {1-5},
  url = {http://strijov.com/papers/MotrenkoStrijovWeber2012SampleSize_ICACM.pdf}
}
Rudoy G.I., Strijov V.V. Simplification of superpositions of primitive functions with graph rule-rewriting // Intellectual Information Processing. Conference proceedings, 2012 : 140-143. InProceedings
Abstract: The paper develops a superposition simplification algorithms for nonlinear regression. A superposition represents an acyclic directed graph. To simplify an graph subtree is replaces for an isomorphic one.
BibTeX:
 
@inproceedings{Rudoy2012IOI, 
  author = {Rudoy, G. I. and Strijov, V. V.},
  title = {Simplification of superpositions of primitive functions with graph rule-rewriting},
  booktitle = {Intellectual Information Processing. Conference proceedings},
  year = {2012},
  pages = {140--143},
  url = {http://strijov.com/papers/Rudoy2012IOI.pdf}
}
Strijov V.V. Sequental model selection in forecasting // 25th European Conference on Operational Research, 2012 : 176. InProceedings
Abstract: To forecast financial time series one needs a set of models of optimal structure and complexity. The mixture model selection procedures are based on the coherent Bayesian inference. To estimate the model parameters and covariance matrix, Laplace approximations methods are introduced. Using the covariance matrix one could split up the data set to form mixture of models and select a model with minimum description length.
BibTeX:
 
@inproceedings{Strijov2012EURO, 
  author = {Vadim V. Strijov},
  title = {Sequental model selection in forecasting},
  booktitle = {25th European Conference on Operational Research},
  year = {2012},
  pages = {176},
  url = {http://strijov.com/papers/Strijov2012EURO.pdf}
}
Tokmakova A.A., Strijov V.V. Estimation of linear model hyperparametres for noise or correlated feature selection problem // Intellectual Information Processing. Conference proceedings, 2012 : 156-159. InProceedings
Abstract: This paper deals with the problem of feature selection in the linear regression models. To select features the author estimate the covariance matrix of the model parameters. Dependent variable and model parameters are assumed to be normally distributed. The laplace approximation is used for estimation the covariance matrix: the logarithm error function is approximated by the normal distribution function. In the case of noise and correlated features covariance matrix becomes singular. An algorithm for feature selection is proposed.
BibTeX:
 
@inproceedings{Tokmakova2012IOI, 
  author = {Tokmakova, A. A. and Strijov, V. V.},
  title = {Estimation of linear model hyperparametres for noise or correlated feature selection problem},
  booktitle = {Intellectual Information Processing. Conference proceedings},
  year = {2012},
  pages = {156-159},
  url = {http://strijov.com/papers/Tokmakova2012IOI.pdf}
}

2011

Krymova E.A., Strijov V.V. Feature selection algorithms for linear regression models from finite and countable sets // Factory laboratory, 2011, 77(5) : 63-68. Article
BibTeX:
 
@article{krymova11algorithmy_zldm, 
  author = {E. A. Krymova and V. V. Strijov},
  title = {Feature selection algorithms for linear regression models from finite and countable sets},
  journal = {Factory laboratory},
  year = {2011},
  volume = {77},
  number = {5},
  pages = {63-68},
  url = {http://zldm.ru/content/article.php?ID=1155}
}
Strijov V.V. Specification of rank-scaled expert estimation using measured data // Factory laboratory, 2011, 77(7) : 72-78. Article
BibTeX:
 
@article{strijov11utochnenie_zldm, 
  author = {Strijov, V. V.},
  title = {Specification of rank-scaled expert estimation using measured data},
  journal = {Factory laboratory},
  year = {2011},
  volume = {77},
  number = {7},
  pages = {72-78},
  url = {http://zldm.ru/content/article.php?ID=1186}
}
Strijov V.V., Granic G., Juric J., Jelavic B., Maricic S.A. Integral indicator of ecological impact of the Croatian thermal power plants // Energy, 2011, 36(7) : 4144-4149. Article
Abstract: The main goal of this paper is to present the methodology of construction of the Integral Indicator for the Croatian Thermal Power Plants and the Combined Heat and Power Plants. The Integral Indicator is intended to compare the Power Plants according to a certain criterion. The criterion of the ecological impact is chosen. The following features of the power plants are used: generated electricity and heat; consumed coal and liquid fuel; sulphur content in fuel; emitted CO2, SO2, NOx, and particles. The linear model is used to construct the Integral Indicator. The model parameters are defined by the Principal Component Analysis. The constructed Integral Indicator is compared with several others, such as Pareto-optimal slicing indicator and Metric indicator. The Integral Indicator keeps as much information about the waste measures of the power plants as possible; it is simple and robust.
BibTeX:
 
@article{strijov10integral_energy, 
  author = {Vadim V. Strijov and Goran Granic and Jeljko Juric and Branka Jelavic and Sandra Antecevic Maricic},
  title = {Integral indicator of ecological impact of the Croatian thermal power plants},
  journal = {Energy},
  year = {2011},
  volume = {36},
  number = {7},
  pages = {4144-4149},
  url = {http://www.sciencedirect.com/science/article/pii/S0360544211002799},
  doi = {10.1016/j.energy.2011.04.030}
}
Strijov V.V., Krymova E.A. Model selection in linear regression analysis // Informational Technologies, 2011, 10 : 21-26. Article
Abstract: To obtain an adequate regression model one often has to enlarge the feature set by generating of derivative features. So the regression problem must be reformulated as the problem of the feature selection. Hereby we assume that the number of features is almost equal of exceeds the number of samples in the data set and present a comparative study of classical and new feature selection algorithms. The study is illustrated by the problem of European option volatility modelling.
BibTeX:
 
@article{krymova11vybor_it, 
  author = {V. V. Strijov and E. A. Krymova},
  title = {Model selection in linear regression analysis},
  journal = {Informational Technologies},
  year = {2011},
  volume = {10},
  pages = {21-26},
  url = {http://novtex.ru/IT/it2011/number_10_annot.html#5}
}
Kuznetsov M.P., Strijov V.V. Integral Indicators and Expert estimations of Ecological Impact // International Conference on Operations Research, 2011 : 32. InProceedings
Abstract: To compare objects or alternative decisions one must evaluate a quality of each object. A real-valued scalar, which is corresponded to the object, is called an integral indicator. The integral indicator of the object is a convolution of the object features. Expert estimations of one expert or an expert group could be indicators, too. We consider a problem of indicator construction as following. There is a set of objects, which should be compared according to a certain quality criterion. A set of features describes each object. This two sets are given together with an «object/feature» matrix of measured data. We select the linear model of the convolution: the integral indicator is the linear combination of features and their weights. So, to construct the integral indicator we must find the weights of the given features. To do that we use the expert estimates of both indicators and weights in rank scales. To compute indicators, according to the linear model, one can use the expert set of weights. In the general case the computed indicators do not match the expert estimations of indicators. Our goal is to match the estimated and the computed integral indicators by maximizing a rank correlation between them. We consider the set of the estimated indicators and the set of the estimated weights as two cones in spaces of indicators and weights, respectively. Our goal is to find the set of weights such that the distance between this set and the cone of the expert-given weights must be minimum. Using the found weights we compute the set of integral indicators such that the distance between this computed set and the cone of the expert-given integral indicators must be minimum, as well. This methodology is used for the Clean Development Mechanism project evaluation. The project partners have to prove that their project can yield emission reductions in developing countries, which could not be achieved in the project’s absence. The proposed integral indicators are intended to evaluate the environmental impact of this projects.
BibTeX:
 
@inproceedings{Kuznetsov2011Integral, 
  author = {Michail P. Kuznetsov and Vadim V. Strijov},
  title = {Integral Indicators and Expert estimations of Ecological Impact},
  booktitle = {International Conference on Operations Research},
  year = {2011},
  pages = {32},
  url = {http://strijov.com/papers/Kuznetsov2011OR.pdf}
}
Kuznetsov M.P., Strijov V.V. Monotonic interpolation for the rank-scaled expert estimations specification // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 : 162-165. InProceedings
BibTeX:
 
@inproceedings{Kuznetsov-Strijov2011Oblique_mmro, 
  author = {M. P. Kuznetsov and V. V. Strijov},
  title = {Monotonic interpolation for the rank-scaled expert estimations specification},
  booktitle = {Proceedings of Mathematical Methods of Pattern Recognition},
  publisher = {МАКС~Пресс},
  year = {2011},
  pages = {162-165},
  url = {http://strijov.com/papers/Kuznetsov2011mmro15.pdf}
}
Pavlov K.V., Strijov V.V. Multilevel model selection in the bank credit scoring applications // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 : 158-161. InProceedings
BibTeX:
 
@inproceedings{Pavlov2011Selection, 
  author = {Pavlov, K. V. and Strijov, V. V.},
  title = {Multilevel model selection in the bank credit scoring applications},
  booktitle = {Proceedings of Mathematical Methods of Pattern Recognition},
  publisher = {МАКС~Пресс},
  year = {2011},
  pages = {158-161},
  url = {http://strijov.com/papers/Pavlov2011mmro15.pdf}
}
Strijov V.V. Multilevel model selection using parameters covariance matrix analysis // Proceedings of Mathematical Methods of Pattern Recognition. МАКС~Пресс, 2011 : 154-157. InProceedings
BibTeX:
 
@inproceedings{Strijov11Multimodel_mmro, 
  author = {Strijov, V. V.},
  title = {Multilevel model selection using parameters covariance matrix analysis},
  booktitle = {Proceedings of Mathematical Methods of Pattern Recognition},
  publisher = {МАКС~Пресс},
  year = {2011},
  pages = {154-157},
  url = {http://strijov.com/papers/Strijov2011mmro15.pdf}
}
Strijov V.V. Invariants and model selection in forecasting // International Conference on Operations Research, 2011 : 133. InProceedings
Abstract: Time series in the financial sector may include annual, weekly and daily periodicals as well as non-periodical events. The energy price and consumed volume time series; the time series of consumer sales volume could be the examples. The generalized linear autoregressive models are used to forecast these time series. The samples of the main time-period of the time series correspond to the features of the forecasting models. To boost the quality of the forecast, two problems must be solved. First, we must select a set of features, which forms the model of optimal quality. Second, we must split the time series on the periodical and eventual segments and assign a model of optimal quality of each type of segments. To solve these problems, we estimate the distribution of the model parameters using coherent Bayesian inference. The optimal model for a given time-segment has the most probable value of maximum evidence, which is estimated under conditions of the stepwise regression: the features are added and deleted from the active feature set towards the evidence maximizing. The splitting procedure includes analysis of the model parameters distributions. Consider two forecasting models that are defined on their non-intersecting consequent time-segments. These models are different if the Kullback-Leibler distance between the distributions of their parameters is statistically significant. In this case the time-segment split is fixed; otherwise we consider the models equal and join the time-segments. The proposed approach brings the most precise time-segment splitting than the dynamic time warping procedure and causes increase of the forecasting quality. As an illustration we discuss the automatic detection of seasonal sales and promotions of consumer goods.
BibTeX:
 
@inproceedings{Strijov2011Invariants_OR, 
  author = {Vadim V. Strijov},
  title = {Invariants and model selection in forecasting},
  booktitle = {International Conference on Operations Research},
  year = {2011},
  pages = {133},
  url = {http://strijov.com/papers/Strijov2011OR.pdf}
}

2010

Strijov V.V., Weber G.W. Nonlinear regression model generation using hyperparameter optimization // Computers and Mathematics with Applications, 2010, 60(4) : 981-988. Article
Abstract: An algorithm of the inductive model generation and model selection is proposed to solve the problem of automatic construction of regression models. A regression model is an admissible superposition of smooth functions given by experts. Coherent Bayesian inference is used to estimate model parameters. It introduces hyperparameters which describe the distribution function of the model parameters. The hyperparameters control the model generation process.
BibTeX:
 
@article{Strijov2010981, 
  author = {Strijov, V. V. and Weber, G. W.},
  title = {Nonlinear regression model generation using hyperparameter optimization},
  journal = {Computers and Mathematics with Applications},
  year = {2010},
  volume = {60},
  number = {4},
  pages = {981-988},
  note = {PCO' 2010 - Gold Coast, Australia 2-4th December 2010, 3rd Global Conference on Power Control Optimization},
  url = {http://www.sciencedirect.com/science/article/B6TYJ-4YX65PS-1/2/471789368d98fd837f293565dbfc0bbb},
  doi = {10.1016/j.camwa.2010.03.021}
}
Strijov V.V. Methods of regression model selection. Moscow, Computing Center RAS, 2010 : 60. Book
Abstract: Problems of regression analysis could be posed as following. First, a repression model and a data generation hypothesis are given. The data generation hypothesis is the distribution function of the random variable as well as assumptions about properties of the random variable. This problem is the optimization problem of the model parameters. Second, a class of the regression models (linear models, radial basic functions, etc.) is given together with a data generation hypothesis. This problem is the problem of model selection. Third, a class of models and a class of data generation hypothesis are given (for example the exponential family of distributions). To solve this problem one must use residual analysis.
BibTeX:
 
@book{strijov2010methody_ccas, 
  author = {Vadim V. Strijov},
  title = {Methods of regression model selection},
  publisher = {Moscow, Computing Center RAS},
  year = {2010},
  pages = {60},
  url = {http://www.machinelearning.ru/wiki/images/5/52/Strijov-Krymova10Model-Selection.pdf}
}
Krymova E.A., Strijov V.V. Model selection and multicollinearity analysis // Proceedings of conference on Intelligent data processing, 2010 : 153-156. InProceedings
BibTeX:
 
@inproceedings{krymova10vybor_ioi, 
  author = {Krymova, E. A. and Strijov, V. V.},
  title = {Model selection and multicollinearity analysis},
  booktitle = {Proceedings of conference on Intelligent data processing},
  year = {2010},
  pages = {153-156},
  url = {http://strijov.com/papers/Krymova2010Select_IOI.pdf}
}
Skipor K.S., Strijov V.V. Least angle logistic regression // Proceedings of conference on Intelligent data processing, 2010 : 180-183. InProceedings
BibTeX:
 
@inproceedings{skipor10method_ioi, 
  author = {Skipor, K. S. and Strijov, V. V.},
  title = {Least angle logistic regression},
  booktitle = {Proceedings of conference on Intelligent data processing},
  year = {2010},
  pages = {180-183},
  url = {http://strijov.com/papers/Skipor2010-iip-8.pdf}
}
Strijov V.V. Evidence of successively generated models // International Conference on Operations Research "Mastering Complexity", 2010 : 223. InProceedings
Abstract: Let us investigate an algorithm of regression model construction. The constructed model will be used to solve problems of the Financial Sector: it might be a scoring model, an energy consumption forecast model or European option volatility smile model. We suppose that given historical data are not sufficient to discover hidden dependencies in an investigated problem. So we propose the following approach to the model construction. Together with historical data we use expert-given set of primitive functions. It is recommended to collect functions, which already widely used to model the investigated problem. Then we assign a generating function, which will be used to generate the set of the competitive models. We estimate evidence of the models using coherent Bayesian inference and select a model of the best structure. Since generating functions make a countable set of models, we organize an iterative generation-selection procedure. Each cycle of the procedure include the following steps. First, we modify competitive models so that the structural distance between an original and a derivative model will as minimal as possible. Second, we estimate parameters and hyperparameters of the derivative model to cut-off some model modifications at the following steps and reduce the algorithm complexity. Third, we analyze the evidence of the derivative model to find the probability to become it a model of the optimal structure. Also, we analyze some restrictions applied to the model structure and robustness of the model. As the result we obtain a model, interpretable from the expert’s point-of view; if fits historical data well and robust. Some additional tests are applied to verify the result model: cross-validation and retrospective forecasting to ensure quality of the further use.
BibTeX:
 
@inproceedings{strijov10evidence_or, 
  author = {Vadim V. Strijov},
  title = {Evidence of successively generated models},
  booktitle = {International Conference on Operations Research "Mastering Complexity"},
  year = {2010},
  pages = {223},
  url = {http://strijov.com/papers/strijov2010OR.pdf}
}
Strijov V.V. Model generation and model selection in credit scoring // 24th European Conference on Operations Research, 2010 : 220. InProceedings
Abstract: The credit scorecard is the logistic regression model; it maps the feature space to the probability of default of a banking client. A classical scorecard is constructed by an analyst, who manually selects informative features and creates combinations of them. We propose a new technique for the automatic scorecard construction. To develop a scorecard, one must assign a set of primitive functions and model generation rules. The result model is an admissible superposition of the primitive functions and features. The coherent Bayesian inference is used to select features and their superpositions.
BibTeX:
 
@inproceedings{strijov10model_euro, 
  author = {Vadim V. Strijov},
  title = {Model generation and model selection in credit scoring},
  booktitle = {24th European Conference on Operations Research},
  year = {2010},
  pages = {220},
  url = {http://strijov.com/papers/strijov10ModelGen_EURO.pdf}
}
Strijov V.V., Krymova E.A., Gerhard W.W. Evidence Optimization for Consequently Generated Models // Proceedings of the fourth global conference on power control and optimization, 2010, 1337 : 204-208. InProceedings
Abstract: We address the problem of segmenting nearly periodic time series into period-like segments. We introduce a definition of nearly periodic time series via triplets hbasic shape, shape transformation, time scalingi that covers a wide range of time series. To split the time series into periods we select a pair of principal components of the Hankel matrix. We then cut the trajectory of the selected principal components by its symmetry axis, thus obtaining half-periods that are merged into segments. We describe a method of automatic selection of periodic pairs of principal components, corresponding to the fundamental periodicity. We demonstrate the application of the proposed method to the problem of period extraction for accelerometric time series of human gait. We see the automatic segmentation into periods as a problem of major importance for human activity recognition problem, since it allows to obtain interpretable segments: each extracted period can be seen as an ultimate entity of gait. The method we propose is more general compared to the application specific methods and can be used for any nearly periodical time series. We compare its performance to classical mathematical methods of period extraction and find that it is not only comparable to the alternatives, but in some cases performs better. Index Terms—sensor signal processing, nearly periodic time series, time series segmentation, period extraction, principal components analysis.
BibTeX:
 
@inproceedings{Strijov2011Evidence_AIP, 
  author = {Strijov, V. V. and Krymova, E. A. and Gerhard, W. W.},
  editor = {Nader Barsoum and Jeffrey Frank Webb and Pandian Vasant},
  title = {Evidence Optimization for Consequently Generated Models},
  booktitle = {Proceedings of the fourth global conference on power control and optimization},
  year = {2010},
  volume = {1337},
  pages = {204-208},
  url = {http://strijov.com/papers/strijov-weber2010PCO-3.pdf},
  doi = {10.1063/1.3592467}
}
Strijov V.V., Letmathe P. Integral indicators based on data and rank-scale expert estimations // Intellectual Data Analysis: the International Scientific Conference Proceedings, 2010 : 107-110. InProceedings
Abstract: Integral indicators play important role in decision making. To make a balanced decision one needs measured data and expert estimations. The expert estimations may contradict the data. Below we investigate a method of integral indicator construction. It uses rank-scaled expert estimations and resolves the possible contradiction between the estimations and the data.
BibTeX:
 
@inproceedings{strijov10integral_ioi, 
  author = {Vadim V. Strijov and Peter Letmathe},
  title = {Integral indicators based on data and rank-scale expert estimations},
  booktitle = {Intellectual Data Analysis: the International Scientific Conference Proceedings},
  year = {2010},
  pages = {107-110},
  url = {http://strijov.com/papers/Strijov2010-iip-8.pdf}
}
Strijov V.V., Weber G.W., Dolgopolova I. Model Generation and Mathematical Modelling // EngOpt 2010: 2nd International Conference on Engineering Optimization, 2010 : 169. InProceedings
Abstract: Mathematical modelling has two issues: first, to create a model of a dynamic system using expert knowledge and second, to discover a model using the measured data. We observe the model-driven and the data-driven approaches to the model creation problem and propose the new combined one. It gathers strong sides of classical approaches: the result model could be explained by experts and it fits measured data well. The new technique is illustrated by the model of pressure in combusting camera of diesel engine.
BibTeX:
 
@inproceedings{strijov10model_engopt, 
  author = {Vadim V. Strijov and Gerhard Wilhelm Weber and Irina Dolgopolova},
  title = {Model Generation and Mathematical Modelling},
  booktitle = {EngOpt 2010: 2nd International Conference on Engineering Optimization},
  year = {2010},
  pages = {169},
  url = {http://lemac1.dem.ist.utl.pt/engopt2010/Book_and_CD/Book_of_Abstracts_Final_Version/Book_abstrats_EngOpt2010.pdf}
}

2009

Strijov V.V., Sologub R.A. The inductive generation of the volatility smile models // Journal of Computational Technologies, 2009, 14(5) : 102-113. Article
Abstract: Volatility of the European-type options depends on their strike and maturity. The authors suppose the volatility smile models based not only expert knowledge, but also on data. The model generation algorithm was proposed. It generates volatility models of the optimal structure inductively using implied volatility data and expert considerations. The models satisfy expert assessments. The Brent Crude Oil option was considered as an example.
BibTeX:
 
@article{strijov09jct, 
  author = {Strijov, V. V. and Sologub, R. A.},
  title = {The inductive generation of the volatility smile models},
  journal = {Journal of Computational Technologies},
  year = {2009},
  volume = {14},
  number = {5},
  pages = {102-113},
  url = {http://strijov.com/papers/Strijov09JCT5.pdf}
}
Krymova E.A., Strijov V.V. Comparison of the heuristic algorithms for linear regression model selection // Mathematical methods for pattern recognition. Conference proceedings. MAKS Press, 2009 : 145-148. InProceedings
BibTeX:
 
@inproceedings{krymova09mmro, 
  author = {Krymova, E. A. and Strijov, V. V.},
  title = {Comparison of the heuristic algorithms for linear regression model selection},
  booktitle = {Mathematical methods for pattern recognition. Conference proceedings},
  publisher = {MAKS Press},
  year = {2009},
  pages = {145-148},
  url = {http://strijov.com/papers/strijov09MM1_MMRO-14.pdf}
}
Melnikov D.I., Strijov V.V., Anderrva E.Y., Edenharter G. Selection of support object set for robust integral indicator construction // // Mathematical methods for pattern recognition. Conference proceedings. MAKS Press, 2009 : 159-162. InProceedings
BibTeX:
 
@inproceedings{melnikov09mmro, 
  author = {Melnikov, D. I. and Strijov, V. V. and Anderrva, E. Yu. and Edenharter, G.},
  title = {Selection of support object set for robust integral indicator construction},
  booktitle = {// Mathematical methods for pattern recognition. Conference proceedings},
  publisher = {MAKS Press},
  year = {2009},
  pages = {159-162},
  url = {http://strijov.com/papers/strijov09MM2_MMRO-14.pdf}
}
Strijov A.V., Strijov V.V. Specification of the rank-scaled expert estimations // Mathematics. Computer. Education. Conference Proceedings, 2009 : 41. InProceedings
Abstract: The algorithm of the integral indicators construction is described. It uses rank-scaled expert estimations and an object-feature data matrix. The expert estimations are specified according to the data and additional expert preferences. To construct integral indicators, linear regression methods are involved. The suggested algorithm is compared with the algorithm of linear-scaled expert estimations concordance.
BibTeX:
 
@inproceedings{strizhov09mce, 
  author = {Strijov, A. V. and Strijov, V. V.},
  title = {Specification of the rank-scaled expert estimations},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2009},
  pages = {41},
  url = {http://strijov.com/papers/strizhov09mce.pdf}
}
Strijov V.V. Model selection using inductively generated set // European Conference on Operational Research EURO-23, 2009 : 114. InProceedings
Abstract: Model selection is one of the most important subjects of Machine learning. An algorithm of model selection depends on the class of models and on the investigated problems. In the lecture the problems of regression analysis will be observed. Linear as well as nonlinear regression models will be considered. The models are supposed to be inductively generated during the selection process. Properties of Lars, Optimal brain surgery and Bayesian coherent inference algorithms will be analyzed in the light of model selection.
BibTeX:
 
@inproceedings{strijov09EURO, 
  author = {Strijov, V. V.},
  title = {Model selection using inductively generated set},
  booktitle = {European Conference on Operational Research EURO-23},
  year = {2009},
  pages = {114},
  url = {http://strijov.com/papers/strijov2009EURO23.pdf}
}
Strijov V.V. Model generation and model selection // Mathematics. Computer. Education. Conference Proceedings, 2009. InProceedings
BibTeX:
 
@inproceedings{strijov09mce, 
  author = {Strijov, V. V.},
  title = {Model generation and model selection},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2009},
  url = {http://strijov.com/papers/strijov09mce.pdf}
}
Strijov V.V. The Inductive Algorithms of Model Generation // SIAM Conference on Computational Science and Engineering, 2009. InProceedings
Abstract: One of the important problems in scientific data mining is the problem of regression modeling. To make a regression model using measured data a researcher examines set of competitive models and chooses a model of the best quality. Due to the nature of the experiments non-linear models are common in biological simulations. Symbolic regression allows dealing with large sets of non-linear models. In the lecture inductive algorithms for model creation and selection will be discussed.
BibTeX:
 
@inproceedings{strijov09SIAMcse09, 
  author = {Strijov, V. V.},
  title = {The Inductive Algorithms of Model Generation},
  booktitle = {SIAM Conference on Computational Science and Engineering},
  year = {2009},
  url = {http://strijov.com/papers/strijov09_SIAM_cse09.pdf}
}
Strijov V.V., Granic G.and Juric Z., Jelavic B., Maricic S. Integral Indicator of Ecological Footprint for Croatian Power Plants // HED Energy Forum “Quo Vadis Energija in Times of Climate Change”, 2009 : 46. InProceedings
Abstract: The main goal of this paper is to present the methodology of construction of the Integral Indicator for Croatian Power Plants. The Integral Indicator is necessary to compare Power Plants selected according to a certain criterion. Herewith the criterion of the Ecological Footprint was chosen. TPP and CHP Power Plants were selected. The following features were used: generated electricity and heat; consumed coal and liquid fuel; sulphur content in fuel; emitted CO2, SO2, NOx and particles. To construct the Integral Indicator the linear model were used. The model was tuned by Principal Component Analysis algorithm. The constructed Integral Indicator was compared with several others, such as Pareto-Optimal Slicing Indicator and Metric Indicator. The Integral Indicator keeps as much information about features of the Power Plants as possible; it is simple and robust.
BibTeX:
 
@inproceedings{strijov09HED, 
  author = {Strijov, V. V. and Granic, G.and Juric, Z. and Jelavic, B. and Maricic, S.A.},
  title = {Integral Indicator of Ecological Footprint for Croatian Power Plants},
  booktitle = {HED Energy Forum “Quo Vadis Energija in Times of Climate Change”},
  year = {2009},
  pages = {46},
  url = {http://strijov.com/papers/IndicatorOfEcoFootprintForCroatianPPs09HED_EIHP.pdf}
}
Strijov V.V., Krymova E.A. Algorithms of linear model generation // Mathematics. Computer. Education. Conference Proceedings, 2009. InProceedings
BibTeX:
 
@inproceedings{krymova09mce, 
  author = {Strijov, V. V. and Krymova, E. A.},
  title = {Algorithms of linear model generation},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2009},
  url = {http://strijov.com/papers/krymova09mce.pdf}
}
Strijov V.V., Sologub R.A. Generation of the implied volatility models // Mathematics. Computer. Education. Conference Proceedings, 2009. InProceedings
BibTeX:
 
@inproceedings{sologub09mce, 
  author = {Strijov, V. V. and Sologub, R. A.},
  title = {Generation of the implied volatility models},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2009},
  url = {http://strijov.com/papers/sologub09mce.pdf}
}
Strijov V.V., Sologub R.A. Algorithm of nonlinear regression model selection by analysis of hyperparameters // Mathematical methods for pattern recognition. Conference proceedings. MAKS~Press, 2009 : 184-187. InProceedings
BibTeX:
 
@inproceedings{strijov09mmro, 
  author = {Strijov, V. V. and Sologub, R. A.},
  title = {Algorithm of nonlinear regression model selection by analysis of hyperparameters},
  booktitle = {Mathematical methods for pattern recognition. Conference proceedings},
  publisher = {MAKS~Press},
  year = {2009},
  pages = {184-187},
  url = {http://strijov.com/papers/strijov09MM3_MMRO-14.pdf}
}

2008

Strijov V.V. The methods for the inductive generation of regression models. Moscow, Computing Center RAS, 2008. Book
BibTeX:
 
@book{strijov08ln, 
  author = {Strijov, V. V.},
  title = {The methods for the inductive generation of regression models},
  publisher = {Moscow, Computing Center RAS},
  year = {2008},
  url = {http://strijov.com/papers/strijov08ln.pdf}
}
Bray D., Strijov V.V. Using immune markers for classification of the CVD patients // Intellectual Data Analysis: Abstracts of the International Scientific Conference, 2008 : 49-50. InProceedings
Abstract: The goal of the investigation is to find an algorithm that successfully separates different groups of patients with Cardio-Vascular Disease. The algorithm must select the most informative features: the markers, which bring the minimal number of the misclassified patients. Four groups of the CVD-patients are considered: A1 (surgery performed), A3 (risk group) and B1, B2 (healthy groups). Each group contained up to 15 patients. Each patient is described with 20 immune markers. Since the number of the patients in the sample is relatively small, the number of the informative markers must not exceed a few to avoid overtraining. The algorithm must process pairs of the classes.
BibTeX:
 
@inproceedings{bray08ioi, 
  author = {Bray, D. and Strijov, V. V.},
  title = {Using immune markers for classification of the CVD patients},
  booktitle = {Intellectual Data Analysis: Abstracts of the International Scientific Conference},
  year = {2008},
  pages = {49-50},
  url = {http://strijov.com/papers/bray08ioi.pdf}
}
Gushchin A.V., Strijov V.V. An algorithm on the expert estimations objectification with measured data // Intellectual Data Analysis: the International Scientific Conference, 2008 : 78-79. InProceedings
BibTeX:
 
@inproceedings{gushchin08ioi, 
  author = {Gushchin, A. V. and Strijov, V. V.},
  title = {An algorithm on the expert estimations objectification with measured data},
  booktitle = {Intellectual Data Analysis: the International Scientific Conference},
  year = {2008},
  pages = {78-79},
  url = {http://strijov.com/papers/gushchin08ioi.pdf}
}
Sologub R.A., Strijov V.V. The inductive construction of the volatility regression models // Intellectual Data Analysis: the International Scientific Conference Proceedings, 2008 : 215-216. InProceedings
BibTeX:
 
@inproceedings{sologub08ioi, 
  author = {Sologub, R. A. and Strijov, V. V.},
  title = {The inductive construction of the volatility regression models},
  booktitle = {Intellectual Data Analysis: the International Scientific Conference Proceedings},
  year = {2008},
  pages = {215-216},
  url = {http://strijov.com/papers/sologub08ioi.pdf}
}
Strijov V.V. On the inductive model generation // Intellectual Data Analysis: Abstracts of the International Scientific Conference, 2008 : 220. InProceedings
Abstract: This talk is devoted to the problem of the automatic model creation in regression analysis. The models are intended for dynamic systems behavior analysis. The theory and the practice of the inductively-generated models will be examined.
BibTeX:
 
@inproceedings{strijov08ioi, 
  author = {Strijov, V. V.},
  title = {On the inductive model generation},
  booktitle = {Intellectual Data Analysis: Abstracts of the International Scientific Conference},
  year = {2008},
  pages = {220},
  url = {http://strijov.com/papers/strijov08ioi.pdf}
}
Strijov V.V. Clusterization of multidimensional time-series using dynamic time warping // Mathematics. Computer. Education. Conference Proceedings, 2008 : 28. InProceedings
BibTeX:
 
@inproceedings{strijov08macoed, 
  author = {Strijov, V. V.},
  title = {Clusterization of multidimensional time-series using dynamic time warping},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2008},
  pages = {28}
}
Strijov V.V. Estimation of hyperparameters on parametric regression model generation // 9th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, 2008, 2 : 178-181. InProceedings
Abstract: The problem of the non-linear regression analysis is considered. The algorithm of the inductive model generation is described. The regression model is a superposi- tion of given smooth functions. To estimate the model parameters two-level Bayesian Inference technique was used. It introduces hyperparameters, which describe the dis- tribution function of the model parameters.
BibTeX:
 
@inproceedings{strijov08roai, 
  author = {Strijov, V. V.},
  title = {Estimation of hyperparameters on parametric regression model generation},
  booktitle = {9th International Conference on Pattern Recognition and Image Analysis: New Information Technologies},
  year = {2008},
  volume = {2},
  pages = {178-181},
  url = {http://strijov.com/papers/strijov08roai_source.pdf}
}
Strijov V.V., Sologub R.A. The inductive generation of the volatility smile models // SIAM Conference on Financial Mathematics and Engineering 2008, 2008 : 21. InProceedings
Abstract: Volatility of the European-type options depends on their strike and maturity. The authors suppose the volatility smile models based not only the expert knowledge, but also on the measured data. The model generation algorithm was proposed. It generates volatility models of the optimal structure inductively using implied volatility data and expert considerations. The models satisfy expert assessments. The Brent Crude Oil option was considered as an example.
BibTeX:
 
@inproceedings{sologub08finance, 
  author = {Strijov, V. V. and Sologub, R. A.},
  title = {The inductive generation of the volatility smile models},
  booktitle = {SIAM Conference on Financial Mathematics and Engineering 2008},
  year = {2008},
  pages = {21},
  url = {http://strijov.com/papers/sologub08finance_eng.pdf}
}
Vorontsov K.V., Inyakin A.S., Lisitsa A., Strijov V.V., Khachay M.Y., Chekhovich Y.V. Proof-ground for classification algorithms: the distributed computing system // Intellectual Data Analysis: the International Scientific Conference, 2008 : 54-56. InProceedings
BibTeX:
 
@inproceedings{vorontsov08polygon, 
  author = {Vorontsov, K. V. and Inyakin, A. S. and Lisitsa, A. and Strijov, V. V. and Khachay, M. Yu. and Chekhovich, Yu. V.},
  title = {Proof-ground for classification algorithms: the distributed computing system},
  booktitle = {Intellectual Data Analysis: the International Scientific Conference},
  year = {2008},
  pages = {54-56}
}
Vorontsov K.V., Inyakin A.S., Strijov V.V., Chekhovich Y.V. MachineLearning.ru: a site, devoted to problems of pattern recognition, forecasting and classification // Intellectual Data Analysis: the International Scientific Conference, 2008 : 56-58. InProceedings
BibTeX:
 
@inproceedings{vorontsov08ml, 
  author = {Vorontsov, K. V. and Inyakin, A. S. and Strijov, V. V. and Chekhovich, Yu. V.},
  title = {MachineLearning.ru: a site, devoted to problems of pattern recognition, forecasting and classification},
  booktitle = {Intellectual Data Analysis: the International Scientific Conference},
  year = {2008},
  pages = {56-58}
}

2007

Strijov V.V. The search for a parametric regression model in an inductive-generated set // Journal of Computational Technologies, 2007, 1 : 93-102. Article
Abstract: The procedure of the search for a regression model is described. The model set is a set of superpositions of smooth functions. The model parameters estimations are used in the search. A model of pressure in a spray chamber of a combustion engine illustrates the approach. In this paper one of the important parts of the proposed project is described.
BibTeX:
 
@article{strijov07jct, 
  author = {Strijov, V. V.},
  title = {The search for a parametric regression model in an inductive-generated set},
  journal = {Journal of Computational Technologies},
  year = {2007},
  volume = {1},
  pages = {93-102},
  url = {http://strijov.com/papers/strijov06poisk_jct_en.pdf}
}
Strijov V.V., Kazakova T.V. Stable indices and the choice of a support description set // Zavodskaya Laboratoriya, 2007, 7 : 72-76. Article
Abstract: This paper describes an integral indicator construction algorithm. The integral indicator is a linear combination of object features. The features are linear-scaled. Outliers among the objects are supposed. The problem of the stable integral indicators construction is posed and solved. To construct the stable integral indicator, a special-defined subset of objects is selected. A nonsupervised algorithm is used to make the integral indicator. The proposed algorithm used to construct an integral indicator of the foodstuff pollution level in Russian regions.
BibTeX:
 
@article{strijov07stable, 
  author = {Strijov, V. V. and Kazakova, T. V.},
  title = {Stable indices and the choice of a support description set},
  journal = {Zavodskaya Laboratoriya},
  year = {2007},
  volume = {7},
  pages = {72-76},
  url = {http://strijov.com/papers/stable_idx4zavlab_after_recenz.pdf}
}
Strijov V.V., Ptashko G.O. Algorithms of the optimal regression model selection. Computing Center of the Russian Academy of Sciences, 2007 : 56. Book
Abstract: A model is defined by a superposition of the smooth functions. The probability density functions of the model parameters are used. The parameters are estimated with non-linear optimization methods. A problem of the diesel engine pressure modelling presents an application of the method. The parametric and non-parametric approaches to model generation are examined. The prototype of the proposed software is described.
BibTeX:
 
@book{strijov06occam, 
  author = {Strijov, V. V. and Ptashko, G. O.},
  title = {Algorithms of the optimal regression model selection},
  publisher = {Computing Center of the Russian Academy of Sciences},
  year = {2007},
  pages = {56},
  url = {http://strijov.com/papers/occam.pdf}
}
Ivakhnenko A.A., Kanevskiy D.Y., Rudeva A.V., Strijov V.V. How to compare marked time-series // Proc. Mathematical Methods of Pattern Recognition, 2007 : 134-137. InProceedings
Abstract: The multi-model regression markup method was described. The markups were used for classification of financial time series.
BibTeX:
 
@inproceedings{strijov07timeseries, 
  author = {Ivakhnenko, A. A. and Kanevskiy, D. Yu. and Rudeva, A. V. and Strijov, V. V.},
  title = {How to compare marked time-series},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2007},
  pages = {134-137},
  url = {http://strijov.com/papers/strijov_MM_AS_4.pdf}
}
Strijov V.V., Kazakova T.V. The rank-scaled expert estimations concordance // Proc. Mathematical Methods of Pattern Recognition, 2007 : 209-211. InProceedings
Abstract: Regression model with restrictions, defined by experts, were described. The new method of multivariate regression modelling was proposed.
BibTeX:
 
@inproceedings{strijov07object, 
  author = {Strijov, V. V. and Kazakova, T. V.},
  title = {The rank-scaled expert estimations concordance},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2007},
  pages = {209-211},
  url = {http://strijov.com/papers/strijov_MM_2.pdf}
}
Strijov V.V., Ptashko G.O. The invariants of time series and dynamic time warping // Proc. Mathematical Methods of Pattern Recognition, 2007 : 212-214. InProceedings
Abstract: Two methods of the regression models usage were compared: the direct regression model and the approximation of the Minimum Cost Path in the Dynamic Time Warping.
BibTeX:
 
@inproceedings{strijov07invariants, 
  author = {Strijov, V. V. and Ptashko, G. O.},
  title = {The invariants of time series and dynamic time warping},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2007},
  pages = {212-214},
  url = {http://strijov.com/papers/strijov_MM_1.pdf}
}

2006

Kazakova T.V., Strijov V.V. The robust indicators with normalising functions selection // Artificial intelligence, 2006, 1 : 160-163. Article
Abstract: The problem of the stable integral indicators is considered. The objects are linear-scaled. To construct a stable integral indicator one has to choose a subset such that the objects in the set bring the maximal value to the criterion of stability. A method of the feature selection according to the regression model robustness was introduced.
BibTeX:
 
@article{strijov06AIidx, 
  author = {Kazakova, T. V. and Strijov, V. V.},
  title = {The robust indicators with normalising functions selection},
  journal = {Artificial intelligence},
  year = {2006},
  volume = {1},
  pages = {160-163},
  url = {http://strijov.com/papers/strijov06AIidx.pdf}
}
Strijov V.V. The search for regression models in an inductive-generated set // Artificial intelligence, 2006, 2 : 234-237. Article
Abstract: The usage of Bayesian inference for the inductive-generated models was described. The algorithm of the arbitrary superpositions of the regression models was introduced. The algorithm uses hyperparameters to estimate the importance of model elements.
BibTeX:
 
@article{strijov06AI, 
  author = {Strijov, V. V.},
  title = {The search for regression models in an inductive-generated set},
  journal = {Artificial intelligence},
  year = {2006},
  volume = {2},
  pages = {234-237},
  url = {http://strijov.com/papers/strijov06AI.pdf}
}
Strijov V.V. Specification of expert estimations using measured data // Factory Laboratory, 2006, 72(7) : 59-64. Article
Abstract: To construct stable integral indicators we will use expert estimations of object features. The indicators are linear combinations of the features. Their values is corrected with the expert estimations. A new method of multivariate regression is described. The model parameters are specified by expert estimations.
BibTeX:
 
@article{strijov06utochnenie_zldm, 
  author = {Strijov, V. V.},
  title = {Specification of expert estimations using measured data},
  journal = {Factory Laboratory},
  year = {2006},
  volume = {72(7)},
  pages = {59-64},
  url = {http://strijov.com/papers/strijov06precise.pdf}
}
Strijov V.V. Vsevolod Vladimirovich Shakin // Mathematics. Computer. Education. Conference Proceedings. Regular and chaotic dynamics, 2006, 1 : 5-16. InCollection
BibTeX:
 
@incollection{strijov06shakin, 
  author = {Strijov, V. V.},
  editor = {Riznichenko, G.~Yu.},
  title = {Vsevolod Vladimirovich Shakin},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  publisher = {Regular and chaotic dynamics},
  year = {2006},
  volume = {1},
  pages = {5-16},
  url = {http://strijov.com/papers/VsevolodShakin06paper.pdf}
}
Kazakova T.V., Strijov V.V. The robust indicators with normalising functions selection // International Scientific Conference on Artificial Intelligence, 2006 : 199. InProceedings
BibTeX:
 
@inproceedings{kazakova06ioi, 
  author = {Kazakova, T. V. and Strijov, V. V.},
  title = {The robust indicators with normalising functions selection},
  booktitle = {International Scientific Conference on Artificial Intelligence},
  year = {2006},
  pages = {199},
  url = {http://strijov.com/papers/strijov_kazakova2006ioi.pdf}
}
Strijov V.V. Indices construction using linear and ordinal expert estimations // Citizens and Governance for Sustainable Development, 2006 : 49. InProceedings
Abstract: Indices are necessary to compare objects united in a set according to a certain criterion. For example, the objects are national protected areas or power plants. An index is a number, which is corresponded to an object. In this research an algorithm for construction of quality indices using expert estimations is developed. Consider an indices construction problem. A set of comparable objects and a set of features are given together with an “object-feature” matrix of measured data. Expert estimations of indices and estimations of importance features are given. A model of indices computation is chosen. In the general case the computed indices don’t coincide with the expert estimates of the indices. The computed importance weights don’t coincide with the expert estimations of importance weights, too. One has to compute indices, which are based on measured data with the condition: the indices must not contradict given expert estimations. There two approaches to the problem were suggested. The first one is the unsupervised indices construction. It finds the model parameters such that provide the maximal value of a selfdescriptiveness criterion. The second approach is the supervised indices construction. The model parameters were set such that provide the minimal value of the distance between the computed indices and their expert estimations. Now the third approach is proposed. According to this approach the experts can resolve the contradiction between expert estimations of indices, importance weights and measured data. At that, there is a hyperparameter embedded in the model. Its value corresponds to importance either the indices or the feature weights.
BibTeX:
 
@inproceedings{strijo06sigsud, 
  author = {Strijov, V. V.},
  title = {Indices construction using linear and ordinal expert estimations},
  booktitle = {Citizens and Governance for Sustainable Development},
  year = {2006},
  pages = {49},
  url = {http://strijov.com/papers/strijo06Abstract_SIGSUD_RuEng.pdf}
}
Strijov V.V. The search for regression models in a set of smooth functions // Mathematics. Computer. Education. Conference Proceedings, 2006. InProceedings
BibTeX:
 
@inproceedings{strijov06mce, 
  author = {Strijov, V. V.},
  title = {The search for regression models in a set of smooth functions},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2006},
  url = {http://strijov.com/papers/strijov06mce.pdf}
}
Strijov V.V. The search for regression models in an inductive-generated set // International Scientific Conference on Artificial Intelligence, 2006 : 198. InProceedings
BibTeX:
 
@inproceedings{strijov2006ioi, 
  author = {Strijov, V. V.},
  title = {The search for regression models in an inductive-generated set},
  booktitle = {International Scientific Conference on Artificial Intelligence},
  year = {2006},
  pages = {198},
  url = {http://strijov.com/papers/strijov2006ioi.pdf}
}
Strijov V.V., Kazakova T.V. Robust indicators and selection of support objects // Multivariate statistical analysis applications in economics and quality assessment. VIII-th International Conference, 2006. InProceedings
BibTeX:
 
@inproceedings{strijovkazakova06CEMI, 
  author = {Strijov, V. V. and Kazakova, T. V.},
  title = {Robust indicators and selection of support objects},
  booktitle = {Multivariate statistical analysis applications in economics and quality assessment. VIII-th International Conference},
  year = {2006},
  url = {http://strijov.com/papers/strijovkazakova06CEMI.pdf}
}

2005

Strijov V.V. Mathematical modelling on the Natural Protected Area management // Actual Problems of Modern Science, 2005, 5 : 79-84. Article
Abstract: The feedback model of The Natural Protected Area management is described. The model involves the subject and the object of management. The subject defines goals and (according to the goals) selects one of the several variants of management. The described feedback model uses annual reports of The Nature Protected Areas and expert estimations. An application of regression analysis in the feedback systems was described.
BibTeX:
 
@article{strijov05model, 
  author = {Strijov, V. V.},
  title = {Mathematical modelling on the Natural Protected Area management},
  journal = {Actual Problems of Modern Science},
  year = {2005},
  volume = {5},
  pages = {79-84},
  note = {ISSN~1680-2721}
}
Kazakova T.V., Strijov V.V. Stable integral indices // Proc. Mathematical Methods of Pattern Recognition, 2005 : 206. InProceedings
BibTeX:
 
@inproceedings{kazakova05mmro, 
  author = {Kazakova, T. V. and Strijov, V. V.},
  title = {Stable integral indices},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2005},
  pages = {206},
  url = {http://strijov.com/papers/kazakova05mmro.pdf}
}
Ptashko G.O., Strijov V.V. The distance function choice for the phase trajectories comparison // Proc. Mathematical Methods of Pattern Recognition, 2005 : 116-119. InProceedings
Abstract: The method of the regression model comparison is examined. При решении задач медицинской диагностики возникает проблема сравнения фазовых траекторий историй болезни пациентов. Предполагается, что пациенты с одинаковым диагнозом имеют сходные траектории. Требуется найти функцию расстояния между траекториями, которая бы удовлетворяла внешнему критерию, задаваемому экспертом. С помощью этой функции создается матрица парных расстояний между траекториями для последующей классификации пациентов по типам болезней.
BibTeX:
 
@inproceedings{ptashko05mmro, 
  author = {Ptashko, G. O. and Strijov, V. V.},
  title = {The distance function choice for the phase trajectories comparison},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2005},
  pages = {116-119},
  url = {http://strijov.com/papers/ptashko05mmro.pdf}
}
Ptashko G.O., Strijov V.V., Shakin V.V. Specification of ordinal expert estimations // Mathematics. Computer. Education. Conference Proceedings, 2005. InProceedings
BibTeX:
 
@inproceedings{ptashko05macoed, 
  author = {Ptashko, G. O. and Strijov, V. V. and Shakin, V. V.},
  title = {Specification of ordinal expert estimations},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2005},
  url = {http://strijov.com/papers/macoed05_2.pdf}
}
Strijov V.V. How to select a nonlinear regression model of optimal complexity? // Proc. Mathematical Methods of Pattern Recognition, 2005 : 190-191. InProceedings
Abstract: A model of optimal complexity was chosen from a set of several thousand inductively-generated models. The Bayesian inference was used.
BibTeX:
 
@inproceedings{strijov05mmro, 
  author = {Strijov, V. V.},
  title = {How to select a nonlinear regression model of optimal complexity?},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2005},
  pages = {190-191},
  url = {http://strijov.com/papers/strijov05mmro.pdf}
}
Strijov V.V., Shakin V.V. Selection of optimal regression model // Mathematics. Computer. Education. Conference Proceedings, 2005. InProceedings
BibTeX:
 
@inproceedings{strijov05macoed, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Selection of optimal regression model},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2005},
  url = {http://strijov.com/papers/macoed05_2.pdf}
}

2003

Strijov V.V., Shakin V.V. Index construction: the expert-statistical method // Environmental research, engineering and management, 2003, 26(4) : 51-55. Article
Abstract: This paper deals with the index construction and presents a new technique that involves expert estimations of object indices as well as feature significance weights. An index is calculated as a linear combination of the object’s features. Non-supervised methods of the index construction are observed to be compared with the new method. Experts can estimate the index and verify the results. The results are precise valid indices and the reasoned expert estimations. This technique was used in various economical, sociological, and ecological applications. This paper introduces a method of multivariate regression model construction. Here an integral indicator is a regression model with applied restrictions.
BibTeX:
 
@article{strijov03index, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Index construction: the expert-statistical method},
  journal = {Environmental research, engineering and management},
  year = {2003},
  volume = {26},
  number = {4},
  pages = {51-55},
  note = {ISSN~1392-1649},
  url = {http://strijov.com/papers/10-v_strijov.pdf}
}
Strijov V.V., Shakin V.V. Forecast and control with autoregressive models // Proc. Mathematical Methods of Pattern Recognition conference, 2003 : 178-181. InProceedings
Abstract: An autoregressive model is represented as the model of dynamic system behavior. One can control the system state using the inverse regression model. The authors use time series to verify the models. Векторные авторегрессионные модели и модели на основе одновременных уравнений являются эффективными инструментами макроэкономического анализа. Ранее была построена модель краткосрочного прогноза основных макроэкономических показателей российской экономики с использованием системы линейных одновременных уравнений. В данной работе для прогноза используется векторно-авторегрессионная модель, составленная таким образом, что значения прогнозной функции зависят не только от экзогенных, сценарных воздействий, но и, в частности, от целевого управления. Новая модель позволяет найти оптимальные управляющие воздействия и спрогнозировать состояние объекта управления при оптимальном управлении.
BibTeX:
 
@inproceedings{strijov03prognoz, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Forecast and control with autoregressive models},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition conference},
  year = {2003},
  pages = {178-181},
  url = {http://strijov.com/papers/mmro11.pdf}
}
Strijov V.V., Shakin V.V. Index construction: the expert-statistical method // Proc. Conference on Sustainability Indicators and Intelligent Decisions, 2003 : 56-57. InProceedings
Abstract: There are lots of ways to construct indices. However, when algorithms are chosen and some results obtained, the following question arises: How to show adequacy of the calculated indices? To answer the question analysts invite experts. The experts express their opinion and then the second question arises: How to show that expert estimations are valid?
BibTeX:
 
@inproceedings{strijov03siid, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Index construction: the expert-statistical method},
  booktitle = {Proc. Conference on Sustainability Indicators and Intelligent Decisions},
  year = {2003},
  pages = {56-57},
  url = {http://strijov.com/papers/siid03.pdf}
}
Aivazian S.A., Strijov V.V., Shakin V.V. On a problem of macroeconomics management. Computing Center of the Russian Academy of Sciences, 2003. TechReport
Abstract: In this paper the application of autoregressive models is considered. The models are used to control the macroeconomic system so that the system obtained a given state. The quality of the control was defined as an integral indicator.
BibTeX:
 
@techreport{aivazian03macro, 
  author = {Aivazian, S. A. and Strijov, V. V. and Shakin, V. V.},
  title = {On a problem of macroeconomics management},
  publisher = {Computing Center of the Russian Academy of Sciences},
  year = {2003},
  url = {http://strijov.com/papers/macro1.pdf}
}

2002

Strijov V.V. Expert estimations concordance for biosystems under extreme conditions. Notes on applied mathematics. Moscow, Coumpiting Center of RAS, 2002. Book
BibTeX:
 
@book{Strijov2002Extreme, 
  author = {Strijov, V. V.},
  title = {Expert estimations concordance for biosystems under extreme conditions. Notes on applied mathematics},
  publisher = {Moscow, Coumpiting Center of RAS},
  year = {2002},
  url = {http://strijov.com/papers/strijov280502.pdf}
}
Molak V., Strijov V.V., Shakin V.V. Kyoto-Index for power plants in the USA // Mathematics. Computer. Education. Conference Proceedings, 2002 : 292. InProceedings
BibTeX:
 
@inproceedings{molak02usa, 
  author = {Molak, V. and Strijov, V. V. and Shakin, V. V.},
  title = {Kyoto-Index for power plants in the USA},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2002},
  pages = {292},
  url = {http://strijov.com/papers/kimacoed02.pdf}
}
Strijov V.V., Shakin V.V. Rank-scaled expert estimations concordance // International Scientific Conference on Artificial Intelligence, 2002 : 82-83. InProceedings
BibTeX:
 
@inproceedings{strijov02ioi, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Rank-scaled expert estimations concordance},
  booktitle = {International Scientific Conference on Artificial Intelligence},
  year = {2002},
  pages = {82-83},
  url = {http://strijov.com/papers/ioi2002.pdf}
}
Strijov V.V., Shakin V.V. Rank-scaled expert estimations processing // Mathematics. Computer. Education. Conference Proceedings, 2002 : 148. InProceedings
BibTeX:
 
@inproceedings{strijov02macoed, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Rank-scaled expert estimations processing},
  booktitle = {Mathematics. Computer. Education. Conference Proceedings},
  year = {2002},
  pages = {148},
  url = {http://strijov.com/papers/MaCoEd2002.pdf}
}
Strijov V.V. Specification of expert estimations for integral indicators construction (thesis abstract). Computing Center of the Russian Academy of Sciences, 2002 : 24. PhdThesis
BibTeX:
 
@phdthesis{strijov02phdreferat, 
  author = {Strijov, V. V.},
  title = {Specification of expert estimations for integral indicators construction (thesis abstract)},
  school = {Computing Center of the Russian Academy of Sciences},
  year = {2002},
  pages = {24},
  note = {Author's abstract},
  url = {http://strijov.com/papers/concorda.pdf}
}
Strijov V.V. Specification of expert estimations for integral indicators construction (thesis manuscript). Computing Center of the Russian Academy of Sciences, 2002 : 105. PhdThesis
Abstract: The supervised and non-supervised index construction methods are investigated. The expert estimation concordance problem is represented as a problem of regression analysis.
BibTeX:
 
@phdthesis{strijov02phdthesis, 
  author = {Strijov, V. V.},
  title = {Specification of expert estimations for integral indicators construction (thesis manuscript)},
  school = {Computing Center of the Russian Academy of Sciences},
  year = {2002},
  pages = {105},
  url = {http://strijov.com/papers/concordt.pdf}
}
Strijov V.V. Time management for development of electronic devices, 2002 : 2. TechReport
BibTeX:
 
@techreport{strijov02RnDelektron, 
  author = {Strijov, V. V.},
  title = {Time management for development of electronic devices},
  year = {2002},
  pages = {2},
  url = {http://strijov.com/papers/RnD_elektron.pdf}
}
Strijov V.V., et al. Methodology elements of the university research effectiveness estimations. Part~1., 2002 : 7. TechReport
BibTeX:
 
@techreport{strijov02effect1, 
  author = {Strijov, V. V. and et~al.},
  title = {Methodology elements of the university research effectiveness estimations. Part~1.},
  year = {2002},
  pages = {7},
  url = {http://strijov.com/papers/part1ver1.pdf}
}
Strijov V.V., et al. Methodology elements of the university research effectiveness estimations. Part~2., 2002 : 7. TechReport
Abstract: In this paper a method of the discrete regression model based on expert estimations was described.
BibTeX:
 
@techreport{strijov02effect2, 
  author = {Strijov, V. V. and et al.},
  title = {Methodology elements of the university research effectiveness estimations. Part~2.},
  year = {2002},
  pages = {7},
  url = {http://strijov.com/papers/part2ver2.pdf}
}
Strijov V.V., Shakin V.V. Ordering of mixed-scaled objects, 2002 : 8. TechReport
BibTeX:
 
@techreport{strijov02ranks, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Ordering of mixed-scaled objects},
  year = {2002},
  pages = {8},
  url = {http://strijov.com/papers/multiscales_indicatros.pdf}
}

2001

Karioukhin E.V., Shakin V.V., Strijov V.V., Matunin E.S., Izgacheva T.S., Kazakova T.V. Mathematical modelling of gerontology-support organizations // The Clinical Gerontology. Scientific Journal. Moscow: Newdiamed, 2001, 7(8) : 89. Article
BibTeX:
 
@article{karioukhin01model, 
  author = {Karioukhin, E. V. and Shakin, V. V. and Strijov, V. V. and Matunin, E. S. and Izgacheva, T. S. and Kazakova, T. V.},
  title = {Mathematical modelling of gerontology-support organizations},
  journal = {The Clinical Gerontology. Scientific Journal},
  publisher = {Moscow: Newdiamed},
  year = {2001},
  volume = {7},
  number = {8},
  pages = {89}
}
Strijov V.V. Bidirectional CBT chips application // Schemotechnics, 2001, 2 : 18-19. Article
BibTeX:
 
@article{strijov01cbt, 
  author = {Strijov, V. V.},
  title = {Bidirectional CBT chips application},
  journal = {Schemotechnics},
  year = {2001},
  volume = {2},
  pages = {18-19},
  url = {http://strijov.com/papers/cbt.pdf}
}
Strijov V.V. CMOS buffers // Schemotechnics, 2001, 2 : 20-21. Article
BibTeX:
 
@article{strijov01cmos, 
  author = {Strijov, V. V.},
  title = {CMOS buffers},
  journal = {Schemotechnics},
  year = {2001},
  volume = {2},
  pages = {20-21},
  url = {http://strijov.com/papers/kmop.pdf}
}
Strijov V.V. Live plug-in // Schemotechnics, 2001, 5 : 15-18. Article
BibTeX:
 
@article{strijov01live, 
  author = {Strijov, V. V.},
  title = {Live plug-in},
  journal = {Schemotechnics},
  year = {2001},
  volume = {5},
  pages = {15-18},
  url = {http://strijov.com/papers/s15-18.pdf}
}
Strijov V.V., Shakin V.V. An algorithm for clustering of the phase trajectory of a dynamic system // Mathematical Communications, Supplement, 2001, 1 : 159-165. Article
Abstract: This paper describes an algorithm of quantitative analysis of the dynamic system behavior. The system behavior is represented as a multivariate phase trajectory. The algorithm clusters the trajectory to satisfy the requirement of the local space dimension. The set of the clusters is represented as an unbalanced tree. The phase trajectory of the Lorenz attractor is examined as a test problem to demonstrate the algorithm. The analysis is intended to describe the behavior of various living systems. The method of the space reduction for the piecewise linear regression models was proposed.
BibTeX:
 
@article{strijov01clustering, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {An algorithm for clustering of the phase trajectory of a dynamic system},
  journal = {Mathematical Communications, Supplement},
  year = {2001},
  volume = {1},
  pages = {159-165},
  url = {http://strijov.com/papers/koi2000a.pdf}
}
Matunin E.S., Izgacheva T.S., Kazakova T.V., Karioukhin E.V., Strijov V.V., Shakin V.V. Mathematical modelling and informational support for gerontology organizations. Computing Center of the Russian Academy of Sciences, 2001 : 79. Book
BibTeX:
 
@book{matunin01ccas, 
  author = {Matunin, E. S. and Izgacheva, T. S. and Kazakova, T. V. and Karioukhin, E. V. and Strijov, V. V. and Shakin, V. V.},
  title = {Mathematical modelling and informational support for gerontology organizations},
  publisher = {Computing Center of the Russian Academy of Sciences},
  year = {2001},
  pages = {79}
}
Molak V., Shakin V.V., Strijov V.V. Kyoto Index for power plants in the USA // The 3-rd Moscow International Conference On Operations Research, 2001 : 80. InProceedings
BibTeX:
 
@inproceedings{molak01kioto, 
  author = {Molak, V. and Shakin, V. V. and Strijov, V. V.},
  title = {Kyoto Index for power plants in the USA},
  booktitle = {The 3-rd Moscow International Conference On Operations Research},
  year = {2001},
  pages = {80}
}
Strijov V.V., Shakin V.V. Expert estimations concordance // Proc. Mathematical Methods of Pattern Recognition, 2001 : 137-138. InProceedings
BibTeX:
 
@inproceedings{strijov01mmro, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Expert estimations concordance},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {2001},
  pages = {137-138},
  url = {http://strijov.com/papers/mmro10.pdf}
}
Strijov V.V., Shakin V.V., Blagovidov K.V. Concordance of expert estimations for analysis of protected areas management effectiveness // Multivariate statistics analysis applications in economics and quality estimation, 2001 : 30. InProceedings
BibTeX:
 
@inproceedings{strijov01cemi, 
  author = {Strijov, V. V. and Shakin, V. V. and Blagovidov, K. V.},
  title = {Concordance of expert estimations for analysis of protected areas management effectiveness},
  booktitle = {Multivariate statistics analysis applications in economics and quality estimation},
  year = {2001},
  pages = {30},
  url = {http://strijov.com/papers/cemi2001.pdf}
}
Zubarevich N.V., Tikunov V.S., Krepets V.V., Strijov V.V., Shakin V.V. Multivariate methods for human development index estimation in Russian regions // GIS for area sustainable development. International Conference Proceedings, 2001 : 84-105. InProceedings
BibTeX:
 
@inproceedings{tikunov01gis, 
  author = {Zubarevich, N. V. and Tikunov, V. S. and Krepets, V. V. and Strijov, V. V. and Shakin, V. V.},
  title = {Multivariate methods for human development index estimation in Russian regions},
  booktitle = {GIS for area sustainable development. International Conference Proceedings},
  year = {2001},
  pages = {84-105}
}
Strijov V.V., Shakin V.V. Analysis of a dynamic system phase trajectory, 2001 : 5. TechReport
BibTeX:
 
@techreport{strijov01clusteringrus, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Analysis of a dynamic system phase trajectory},
  year = {2001},
  pages = {5},
  url = {http://strijov.com/papers/lorenz-ru.pdf}
}
Strijov V.V., Shakin V.V., Blagovidov K.V. Analysis of protected areas management effectiveness, 2001 : 11. TechReport
BibTeX:
 
@techreport{strijov01ar, 
  author = {Strijov, V. V. and Shakin, V. V. and Blagovidov, K. V.},
  title = {Analysis of protected areas management effectiveness},
  year = {2001},
  pages = {11},
  note = {Lecture notes},
  url = {http://strijov.com/papers/cemi01ar.pdf}
}
Strijov V.V., Shakin V.V., Blagovidov K.V. A model of the Protected Areas Management, 2001 : 7. TechReport
BibTeX:
 
@techreport{strijov01model, 
  author = {Strijov, V. V. and Shakin, V. V. and Blagovidov, K. V.},
  title = {A model of the Protected Areas Management},
  year = {2001},
  pages = {7},
  note = {Manuscript},
  url = {http://strijov.com/papers/pamodel.pdf}
}

2000

Strijov V.V. Square pulse generators with CMOS ICs // Schemotechnics, 2000, 3 : 25-26. Article
BibTeX:
 
@article{strijov00cmosgen, 
  author = {Strijov, V. V.},
  title = {Square pulse generators with CMOS ICs},
  journal = {Schemotechnics},
  year = {2000},
  volume = {3},
  pages = {25-26},
  url = {http://strijov.com/papers/gen_prym.pdf}
}
Strijov V.V. The IC behavior under low-voltage // Schemotechnics, 2000, 2 : 32-33. Article
BibTeX:
 
@article{strijov00lowvoltage, 
  author = {Strijov, V. V.},
  title = {The IC behavior under low-voltage},
  journal = {Schemotechnics},
  year = {2000},
  volume = {2},
  pages = {32-33}
}
Strijov V.V. The simplest PCI interface // Schemotechnics, 2000, 1 : 55-57. Article
BibTeX:
 
@article{strijov00pci, 
  author = {Strijov, V. V.},
  title = {The simplest PCI interface},
  journal = {Schemotechnics},
  year = {2000},
  volume = {1},
  pages = {55-57},
  url = {http://strijov.com/papers/pci.pdf}
}
Strijov V.V. Logic IC with 3V power supply // Schemotechnics, 2000, 3 : 14-15. Article
BibTeX:
 
@article{strijov00treevolt, 
  author = {Strijov, V. V.},
  title = {Logic IC with 3V power supply},
  journal = {Schemotechnics},
  year = {2000},
  volume = {3},
  pages = {14-15},
  url = {http://strijov.com/papers/log_micro.pdf}
}
Strijov V.V., Shakin V.V. An algorithm for clustering of the phase trajectory of a dynamic system // 8-th International Conference on Operational Research, KOI-2000, 2000 : 35. InProceedings
Abstract: This paper describes an approach to quantitative analysis of multivariate dynamic system in phase space. The system is used as mathematical model for various living systems. The model is used in various applications. One of the related problems is to represent a phase trajectory as a sequence of clusters to classify the system's state.
BibTeX:
 
@inproceedings{strijov00koi, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {An algorithm for clustering of the phase trajectory of a dynamic system},
  booktitle = {8-th International Conference on Operational Research, KOI-2000},
  year = {2000},
  pages = {35},
  url = {http://strijov.com/papers/koi2000.pdf}
}

1999

Strijov V.V. Phase trajectory analysis software and its applications // Problems of the complex system safety control. VII-th International Conference Proceedings, 1999 : 156-157. InProceedings
BibTeX:
 
@inproceedings{strijov99rggu, 
  author = {Strijov, V. V.},
  title = {Phase trajectory analysis software and its applications},
  booktitle = {Problems of the complex system safety control. VII-th International Conference Proceedings},
  year = {1999},
  pages = {156-157},
  url = {http://strijov.com/papers/safety99.pdf}
}
Strijov V.V., Shakin V.V. Phase trajectory analysis software // Proc. Mathematical Methods of Pattern Recognition, 1999 : 227-230. InProceedings
BibTeX:
 
@inproceedings{strijov99soft, 
  author = {Strijov, V. V. and Shakin, V. V.},
  title = {Phase trajectory analysis software},
  booktitle = {Proc. Mathematical Methods of Pattern Recognition},
  year = {1999},
  pages = {227-230},
  url = {http://strijov.com/papers/mmro9.pdf}
}

1997

Strijov V.V. Motorola IC for TV, video and multimedia overview. Moscow: Motorola GmbH, 1997 : 75. Book
BibTeX:
 
@book{strijov97motorola, 
  author = {Strijov, V. V.},
  title = {Motorola IC for TV, video and multimedia overview},
  publisher = {Moscow: Motorola GmbH},
  year = {1997},
  pages = {75},
  url = {http://strijov.com/papers/motmult.pdf}
}

1996

Strijov V.V. Configurable processors for biomedical data visualizing // Biosystems under extreme conditions. Computing Center of the Russian Academy of Sciences, 1996 : 47-50. InCollection
BibTeX:
 
@incollection{strijov08biorecon, 
  author = {Strijov, V. V.},
  editor = {Shakin, V.},
  title = {Configurable processors for biomedical data visualizing},
  booktitle = {Biosystems under extreme conditions},
  publisher = {Computing Center of the Russian Academy of Sciences},
  year = {1996},
  pages = {47-50},
  url = {http://strijov.com/papers/biorecon.pdf}
}