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. | Yugoslav Journal of Operations Research xx (2018), Number nn, zzz–zzz DOI: GROUP APPROACH TO SOLVING THE TASKS OF RECOGNITION AMIRGALIYEV YEDILKHAN Institute of Information and Computational Technologies, SC MES RK, Almaty. amir ed@ BERIKOV VLADIMIR Sobolev Institute of Mathematics, SB RAS, Novosibirsk, Novosibirsk State University berikov@ CHERIKBAYEVA . Alfarabi Kazakh National University, Almaty nenad@ LATUTA KONSTANTIN Suleyman Demirel University, Almaty BEKTURGAN KALYBEKUULY Institute of Automation and Information Technology of Academy of Science Kyrguz Republic yky198@ Received: July 2018 / Accepted: November 2018 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. Keywords: Recognition, Classification, Hyper Spectral Image, Semi-Supervised Learning. 2 Amirgaliyev, Y., et al. / Group Approach to Solving the Tasks of Recognition MSC: 90B85, 90C26. 1. INTRODUCTION In recent decades, there has been a growing interest in machine learning and data mining. In contrast to classical methods of data analysis, in this area much attention is paid to modeling human behavior, solving complex intellectual problems of generalization, revealing patterns, finding associations, etc. The development of this area was boosted by the ideas arising from the theory of artificial intelligence. The goal of pattern recognition is to classify objects into several classes.
