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Solving classification problems using supervised self-organizing map
Author(s)
Kiatwuthiamorn, Jiraporn
Date Issued
December 1, 2007
Type
Conference Paper
Abstract
This paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self-organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging. ©2007 IEEE.
Citation
Isspit 2007 2007 IEEE International Symposium on Signal Processing and Information Technology, 357-360, 2007