Solving Classification Problems Using Supervised Self-Organizing Map

Author

Thammano, Arit ; Kiatwuthiamorn, Jiraporn

Author_Institution

King Mongkut´´s Inst. of Technol. Ladkrabang, Bangkok

fYear

2007

fDate

15-18 Dec. 2007

Firstpage

357

Lastpage

360

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.

Keywords

fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); pattern classification; pattern clustering; pattern matching; self-organising feature maps; Euclidean distance; fuzzy entropy concept; fuzzy membership function; pattern classification problem; pattern matching; supervised Kohonen self-organizing map; Artificial neural networks; Computational intelligence; Electronic mail; Euclidean distance; Fuzzy neural networks; Information technology; Laboratories; Neural networks; Predictive models; Signal processing; Classification; Data Mining; Neural Network; Self-Organizing Map;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Information Technology, 2007 IEEE International Symposium on

Conference_Location

Giza

Print_ISBN

978-1-4244-1835-0

Electronic_ISBN

978-1-4244-1835-0

Type

conf

DOI

10.1109/ISSPIT.2007.4458036

Filename

4458036