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Use of Fuzzy Clustering Algorithms Ensemble for SVM Classifier Development

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This paper represents an approach for object's classification based on the combined implementation of the SVM algorithm and the fuzzy clustering algorithms. It is offered to use the fuzzy clustering algorithms’ ensemble based on the clusters’ tags’ vectors’ similarity matrixes and the spectral factorization algorithm as for creation the training and test sets, used for development of SVM classifier, as for specification of the clustering results with application of the trained SVM classifier. In the first case we solve an absence problem of aprioristic information on objects’ possible class association and avoid inclusion of noise objects in the training and test sets, that will provide development of more exact SVM classifier. In the second case we use part of the clustering results received by means of the fuzzy clustering algorithms’ ensemble for training of SVM classifier, and another – for the specification of the clustering results by means of the trained SVM classifier. We use in the spectral factorization algorithm along with the k-means algorithm, which is usually applied to graph splitting into clusters, the FCM algorithm, allowing to reduce the cost of graph splitting and receive better graph splitting into clusters, that will provide higher quality of objects’ classification.
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Classification; FCM Algorithm; PCM Algorithm; PFCM Algorithm; SVM Algorithm; Training Set; Testing Set; Ensemble

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Vapnik,V. Statistical Learning Theory (1998) Wiley, New York.

Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S., Choosing Multiple Parameters for Support Vector Machine, (2002) Machine Learning, 46 (1–3), pp. 131–159.

L. Yu, S. Wang, K.K. Lai and L. Zhou, BioInspired Credit Risk Analysis (Springer, 2008).

Raikwal, J.S., Saxena, K., Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set, (2012) International Journal of Computer Applications, 50(14), pp. 35–39.

Joachims, T., Text Categorization with Suport Vector Machines: Learning with Many Relevant Features, (1998) Lecture Notes in Computer Science, 1398, pp. 137–142.

Li, Y., Bontcheva, K., Cunningham, H., SVM Based Learning System For Information Extraction, (2005) Lecture Notes in Computer Science, 3635, pp. 319–339.

LeCun,Y. Jackel, L.D., Bottou,L., Cortes, C. at al., Learning Algorithms for Classification: A Comparison on Handwritten Digit Recognition, (1995) Neural Networks: The Statistical Mechanics Perspective, Oh, J. H., Kwon, C. and Cho, S. (Ed.), World Scientific, pp. 261–276.

Osuna, E., Freund, R., Girosi, F. Training Support Vector Machines: An Application to Face Detection (1997) 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136

Oren, M., Papageorgious, C., Sinha, P., Osuna, E., Poggio, T., Pedestrian Detection Using Wavelet Templates, (1997) 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 193–199.

Schölkopf, B., Smola, A. J., Williamson, R. C., Bartlett, P. L. New Support Vector Algorithms (2000) Neural Computation, 12(5), pp. 1207–1245.

Platt, J.C. Fast Training of Support Vector Machines Using Sequential Minimal Optimization, (1998) Advances in Kernel Methods – Support Vector Learning, pp. 185–208.

Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., Murthy, K. R. K., Improvements to the SMO Algorithm for SVM Regression (2000) IEEE Transactions on Neural Networks, 11(5), pp. 1188–1193.

Osuna, E., Freund, R., Girosi, F., Improved Training Algorithm for Support Vector Machines (1997) 1997 IEEE Workshop Neural Networks for Signal Processing, pp. 24–26, 1997.

Vishwanathan, S.V.N., Smola, A., Murty, N., SSVM: a simple SVM algorithm (2002) Proceedings of the 2002 International Joint Conference on Neural Networks, 3, pp. 2393-2398.

Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A., Pegasos: Primal Estimated sub-GrAdient SOlver for SVM, (2011) Mathematical Programming, 127(1), pp. 3–30.

Bottou, L., Lin, C.-J., Support Vector Machine Solvers (2007) MIT Press, pp. 1–28.

Eads, D. R., Hill, D., Davis, S., Perkins, S. J., Ma, J., Porter, R. B., Theiler, J. P., Genetic algorithms and support vector machines for time series classification, (2002) Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, 74

Lessmann, S., Stahlbock, R., Crone, S. F., Genetic algorithms for support vector machine model selection, (2006) 2006 IJCNN'06. International Joint Conference on Neural Networks, pp. 3063-3069.

Bezdek, J. C., Ehrlich, R., Full, W., FCM: The fuzzy c-means clustering algorithm (1984) Computers & Geosciences, 10(2), pp. 191-203.

Krishnapuram, R., Keller, J.M., The possibilistic c-means algorithm: insights and recommendations (1996) IEEE Transactions on Fuzzy Systems, 4(3), pp. 385-393.

Pal, N. R., Pal, K., Bezdek, J. C., A Mixed C-Means Clustering Model (1997) Sixth IEEE International Conference on Fuzzy Systems, 1, pp. 11–21.

Demidova, L.A., Nesterov, N.I., Fuzzy and Possibilistic Segmentation of Earth Surface Images by Means of Intelligent Information Technologies (2014) 2014 International Conference On Computer Technologies In Physical And Engineering Applications, pp. 35–36.

Strehl, A., Ghosh, J., Cluster Ensembles – a Knowledge Reuse Framework for Combining Multiple Partitions, (2003) Journal of Machine Learning Research, 3, pp. 583–617.

Karypis, G., Kumar, V., A fast and high quality multilevel scheme for partitioning irregular graphs (1998) SIAM Journal of Scientific Computing, 20(1), pp. 359–392, 1998.

Astakhova, N.N., Demidova, L.A., Nikulchev, E.V. Forecasting method for grouped time series with the use of k-means algorithm (2015) Applied mathematical sciences, 9(97), pp. 4813–4830.

Karatzoglou, A., Meyer, D., Hornik, K. Support vector machines in R, (2005). Research Report, WU Vienna University of Economics and Business, Vienna.

Hespanha, J.P. An Efficient MATLAB Algorithm for Graph Partitioning (2004) Technical Report, University of California.

Goldberg, D.E. Korb, B., Deb K., Messy genetic algorithms: Motivation, analysis, and first results (1989) Complex Systems, 5(3), pp. 493–530.

Janikow C.Z., Michalewicz Z., An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms, (1991) Fourth International Conference on Genetic Algorithms, pp. 31–36.

Srinivas, M., Patnaik, L. M., Srinivas. M and Patnaik. L, Adaptive probabilities of crossover and mutation in genetic algorithms (1994) IEEE Transactions on System, Man and Cybernetics, 24(4), pp. 656–667.

Zhang J., Chung, H., Lo, W.L. Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms (2007) IEEE Transactions on Evolutionary Computation, 11(3), pp. 326–335.

Hsu, C.-W., Lin, C.-J. A comparison of methods for multi-class support vector machines (2003) IEEE Transactions on Neural Networks, 13(2), 415–425.


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