Summary
Prototype based rules is an interesting tool for data analysis. However most of prototype selection methods like CFCM+LVQ algorithm do not have embedded feature selection methods and require feature selection as initial preprocessing step. The problem that appears is which of the feature selection methods should be used with CFCM+LVQ prototype selection method, and what advantages or disadvantages of certain solutions can be pointed out. The analysis of the above problems is based on empirical data analysis.
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References
Duch, W.: Similarity based methods: a general framework for classification approximation and association. Control and Cybernetics 29, 937–968 (2000)
Duch, W., Blachnik, M.: Fuzzy rule-based systems derived from similarity to prototypes. In: Pal, N., Kasabov, N., Mudi, R., Pal, S., Parui, S. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 912–917. Springer, Heidelberg (2004)
Blachnik, M., Duch, W., Wieczorek, T.: Selection of prototypes rules context searching via clustering. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS(LNAI), vol. 4029, pp. 573–582. Springer, Heidelberg (2006)
Shridhar, D., Bartlett, E., Seagrave, R.: Information theoretic subset selection. Computers in Chemical Engineering 22, 613–626 (1998)
Shanonn, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press (1946)
Setiono, R., Liu, H.: Improving backpropagation learning with feature selection. The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies 6, 129–139 (1996)
de Mantaras, R.L.: A distance-based attribute selecting measure for decision tree induction. Machine Learning 6, 81–92 (1991)
Chi, J.: Entropy based feature evaluation and selection technique. In: Proc. of 4-th Australian Conf. on Neural Networks (ACNN 1993) (1993)
Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Proceedings of The Twentieth International Conference on Machine Learning (2003)
Duch, W., Biesiada, J.: Feature selection for high-dimensional data: A kolmogorov-smirnov correlation-based filter solution. In: Advances in Soft Computing, pp. 95–104. Springer, Heidelberg (2005)
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Blachnik, M. (2009). Comparison of Various Feature Selection Methods in Application to Prototype Best Rules. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_31
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DOI: https://doi.org/10.1007/978-3-540-93905-4_31
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