PARALLEL MULTIAGENT METHOD OF BIG DATA REDUCTION FOR PATTERN RECOGNITION

Authors

  • A. О. Oliinyk Zaporizhzhia National Technical University, Ukraine
  • S. Yu. Skrupsky Zaporizhzhia National Technical University, Ukraine
  • V. V. Shkarupylo Zaporizhzhia National Technical University, Ukraine
  • O. Yu. Blagodariov Zaporizhzhia National Technical University, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2017-2-9

Keywords:

Аgent, data set, feature selection, parallel computing, multi-agent approach, pattern recognition

Abstract

Context. The problem of feature selection for big data processing based on the multi-agent approach and parallel computation has been solved. The object of research is the process of feature selection. The subject of the research are the methods of feature selection.

Objective. The purpose of the work is to create a parallel multi-agent method for reducing of big data sets.

Method. The article deals with the parallel multi-agent method for reducing of big data sets. The developed method involves splitting multiple agents into several subsets for parallel search of an informative combination of features in different areas of the search space. At the same time, it is suggested that the parallel nodes of the computer system perform the most resource-intensive operations associated with estimating the current set of agents, as well as the need to create and modify new sets of solutions based on stochastic computations. This allows to speed up the process of multi-agent search of informative combination of features, as well as to reduce the practical threshold for application of the multi-agent method with indirect communication between agents for reducing big data sets.

Results. The software which implements the proposed method and allows to select informative features based on the multi-agent approach and parallel computation has been developed.

Conclusions. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in practice for solving the problems of big data processing for pattern recognition. The prospects for further research may include the modification of the developed parallel method for feature selection by using different criteria for estimation of the group information of features, as well as an experimental study of proposed method on more complex practical problems of different nature and dimensionality.

Author Biographies

A. О. Oliinyk, Zaporizhzhia National Technical University

PhD., Associate Professor of Department of Software Tools

S. Yu. Skrupsky, Zaporizhzhia National Technical University

PhD, Associate Professor of Computer Systems and Networks Department

V. V. Shkarupylo, Zaporizhzhia National Technical University

PhD, Associate Professor of Computer Systems and Networks Department

O. Yu. Blagodariov, Zaporizhzhia National Technical University

Postgraduate Student of Department of Software Tools

References

Salfner F., Lenk M., Malek M. A survey of online failure prediction methods, ACM computing surveys, 2010, Vol. 42, Issue 3, pp. 1–42. DOI: 10.1145/1670679.1670680.

Bezdek J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. N.Y. Plenum Press, 1981, 272 p. DOI: 10.1007/ 978-1-4757-0450-1.

Bow S. Pattern recognition and image preprocessing. New York, Marcel Dekker Inc., 2002, 698 p. DOI: 10.1201/9780203903896.

Shin Y. C., Xu C. Intelligent systems : modeling, optimization, and control. Boca Raton: CRC Press, 2009, 456 p. DOI: 10.1201/9781420051773.

Bishop C. M. Pattern recognition and machine learning, New York, Springer, 2006, 738 p.

Sammut C., Webb G. I. eds. Encyclopedia of machine learning. New York, Springer, 2011, 1031 p. DOI: 10.1007/978-0-387-30164-8.

Abonyi J., Feil B. Cluster analysis for data mining and system identification. Basel, Birkh user, 2007, 303 p.

Jensen R., Shen Q. Computational intelligence and feature selection: rough and fuzzy approaches. Hoboken, John Wiley & Sons, 2008, 339 p. DOI: 10.1002/9780470377888.

Lee J. A., Verleysen M. Nonlinear dimensionality reduction. New York, Springer, 2007, 308 p. DOI: 10.1007/978-0-387-39351-3.

Bodyanskiy Ye., Tyshchenko O., Kopaliani D. A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade, Int. Journal of Information Technology and Computer Science (IJITCS), 2014, Vol. 6, No. 8, pp. 11–17. DOI: 10.5815/ijitcs.2014.08.02

Oliinyk A. Production rules extraction based on negative selection, Radio Electronics, Computer Science, Control, 2016, No. 1, pp. 40–49. DOI: 10.15588/1607-3274-2016-1-5.

Oliinyk A., Subbotin S. A. The decision tree construction based on a stochastic search for the neuro-fuzzy network synthesis, Optical Memory and Neural Networks (Information Optics), 2015, Vol. 24, No. 1, pp. 18–27. DOI: 10.3103/S1060992X15010038.

Oliinyk A., Subbotin S. A. Association Rules Extraction for Pattern Recognition, Pattern Recognition and Image Analysis, 2016, Vol. 26, No. 2, pp. 419–426.

Oliinyk A. O., Oliinyk O. O. and Subbotin S. A. Agent technologies for feature selection, Cybernetics and Systems Analysis, 2012, Vol. 48, Issue 2, pp. 257–267. DOI: 10.1007/s10559-012-9405-z.

Jolliffe I. T. Principal Component Analysis. Berlin, Springer-Verlag, 2002, 489 p.

McLachlan G. Discriminant Analysis and Statistical Pattern Recognition. New Jersey, John Wiley & Sons, 2004, 526 p.

Guyon I., Elisseeff A. An introduction to variable and feature selection, Journal of machine learning research, 2003, No. 3, pp. 1157–1182.

Kim D. H., Cho C. H. Bacterial Foraging Based Neural Network Fuzzy Learning, Proceedings of the 2nd Indian International Conference on Artificial Intelligence (IICAI-2005). Pune, IICAI, 2005, pp. 2030–2036.

Subbotin S., Oliinyk A., Oliinyk O. Noniterative, evolutionary and multi-agent methods of fuzzy and neural network models synthesis : monograph. Zaporizhzhya, ZNTU, 2009, 375 p. (In Ukrainian).

Subbotin S. A. Synthesis of neuro-fuzzy models for the allocation and detection of objects on a complex background on the twodimensional image, Computer modeling and intelligent systems : proceedings of the conference. Zaporizhzhya, ZNTU, 2007, pp. 68–91.

How to Cite

Oliinyk A. О., Skrupsky, S. Y., Shkarupylo, V. V., & Blagodariov, O. Y. (2017). PARALLEL MULTIAGENT METHOD OF BIG DATA REDUCTION FOR PATTERN RECOGNITION. Radio Electronics, Computer Science, Control, (2), 82–92. https://doi.org/10.15588/1607-3274-2017-2-9

Issue

Section

Neuroinformatics and intelligent systems

Most read articles by the same author(s)