Group approach to solving the tasks of recognition

Authors

  • Yedilkhan Amirgaliyev Institute of Information and Computational Technologies, SC MES RK, Almaty
  • Vladimir Berikov Sobolev Institute of Mathematics, SB RAS, Novosibirsk, Novosibirsk State University
  • Lyailya S. Cherikbayeva Alfarabi Kazakh National University, Almaty
  • Konstantin Latuta Suleyman Demirel University, Almaty
  • Kalybekuuly Bekturgan Institute of Automation and Information Technology of Academy of Science Kyrguz Republic

DOI:

https://doi.org/10.2298/YJOR180822032Y

Keywords:

recognition, classification, hyper spectral image, semi-supervised learning

Abstract

In this work, we develop CASVM and CANN algorithms for semi-supervised classification problem. The algorithms are based on a combination of ensemble clustering and kernel methods. Probabilistic model of classification with use of cluster ensemble is proposed. Within the model, error probability of CANN is studied. Assumptions that make probability of error converge to zero are formulated. The proposed algorithms are experimentally tested on a hyperspectral image. It is shown that CASVM and CANN are more noise resistant than standard SVM and kNN.

References

Amirgaliev, N., Mukhamedgaliev, A.F., On optimization model of classification algorithms, USSR Computational Mathematics and Mathematical Physics, 25 (6) (1985) 95-98.

Aidarkhanov, M.B., Amirgaliev, E.N., La, L.L., Correctness of algebraic extensions of models of classification algorithms, Cybernetics and Systems Analysis, 37 (5) (2001) 777-781.

Amirgaliyev, Y., Hahn, M., and Mussabayev, T., The speech signal segmentation algorithm using pitch synchronous analysis, Open Computer Science, 7 (1) (2017) 1-8.

Amirgaliyev, Y., Nusipbekov, A., Minsoo Hahn, Kazakh Traditional Dance Gesture Recognition, Journal of Physics: Conference Series, 495 (4) (2014) 012036.

Ghosh, J., Acharya, A., Cluster ensembles, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1 (4) (2011) 305-315.

Domeniconi, C., and Al-Razgan, M., Weighted cluster ensembles: Methods and analysis, ACM Transactions on Knowledge Discovery from Data, 2 (4) (2009) 1-17.

Nimesha, M., Patil, D.V., Patil, A., A Survey on K-means Based Consensus Clustering, IJETT, 3 (1) (2016) 40-44.

Topchy, A., Law, M., Jain, A., Fred, A., Analysis of consensus partition in cluster ensemble, Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM04), (2004) 225-232.

Vega-Pons, S., Correa-Morris, J., Ruiz-Shulcloper, J., Weighted cluster ensemble using a kernel consensus function, LNAI, 5197 (2008) 195-202.

Zhu, X., Semi-supervised learning literature survey, Tech. Rep., Department of Computer Science, University of Wisconsin, Madison, 2008.

Berikov, V.B., Weighted ensemble of algorithms for complex data clustering, Pattern Recognition Letters, 38 (2014) 99-106.

Berikov, V., Pestunov, I., Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties, Pattern Recognition, 63 (2017) 27-436.

Vapnik, V.N., Restoration of dependencies according to empirical data, Information Statistics and Statistics, Springer, 1979. 448 p.

Mercer, J., Functions of positive and negative type and their connection with the theory of integral equations, Proceedings of the Royal Society, London, 1909.

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Published

2019-05-01

Issue

Vol. 29 No. 2 (2019)

Section

Research Articles

License

Copyright (c) 2019 YUJOR

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