Abstract
This paper proposes two new concepts: (1) the new evolutionary algorithm and (2) the new approach to deal with the classification problems by applying the concepts of the fuzzy c-means algorithm and the evolutionary algorithm to the artificial neural network. During training, the fuzzy c-means algorithm is initially used to form the clusters in the cluster layer; then the evolutionary algorithm is employed to optimize those clusters and their parameters. During testing, the class whose cluster node returns the maximum output value is the result of the prediction. This proposed model has been benchmarked against the standard backpropagation neural network, the fuzzy ARTMAP, C4.5, and CART. The results on six benchmark problems are very encouraging.
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References
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge Discovery and Data Mining: Towards a Unifying Framework. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (1996)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Academic Press, London (2001)
Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annual Eugenics 7(Part II), 179–188 (1936)
Deterding, D.H.: Speaker Normalization for Automatic Speech Recognition. Ph.D. Dissertation (1989)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science. University of California (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Statlog Project Datasets (Retrieved March 28, 2003), From http://www.liacc.up.pt/ML/statlog/datasets/heart/
Ventura, D., Martinez, T.R.: An Empirical Comparison of Discretization Methods. In: Proceedings of the Tenth International Symposium on Computer and Information Sciences, pp. 443–450 (1995)
Tschichold-Gürman, N.: Fuzzy RuleNet: An Artificial Neural Network Model for Fuzzy Classification. In: Proceedings of the 1994 ACM Symposium on Applied Computing, pp. 145–149 (1994)
Datasets used for Classification: Comparison of Results (Retrieved March 5, 2005), From http://www.phys.uni.torun.pl/kmk/projects/datasets.html
Last, M., Maimon, O.: A Compact and Accurate Model for Classification. IEEE Transactions on Knowledge and Data Engineering 16(2), 203–215 (2004)
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Thammano, A., Meengen, A. (2005). A New Evolutionary Neural Network Classifier. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_31
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DOI: https://doi.org/10.1007/11430919_31
Publisher Name: Springer, Berlin, Heidelberg
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