Solving classification problems using supervised self-organizing map

被引：0

作者：

Thammano, Arft ^{[1
]}

Kiatwuthiamorn, Jirapom ^{[1
]}

机构：

[1] King Mongkuts Inst Technol Ladkrabang, Fac Informat Technol, Computat Intelligence Lab, Bangkok 10520, Thailand

来源：

2007 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, VOLS 1-3 | 2007年

关键词：

classification; neural network; self-organizing map; data mining;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：236 / 239

页数：4

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