Universiti Teknologi Malaysia Institutional Repository

Multistrategy self-organizing map learning for classification problems

Hasan, S. and Shamsuddin, S. M. (2011) Multistrategy self-organizing map learning for classification problems. Computational Intelligence and Neuroscience, 2011 . 11 pg..

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Official URL: http://dx.doi.org/10.1155/2011/121787

Abstract

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test

Item Type:Article
Additional Information:Article ID 121787
Subjects:Unspecified
Divisions:Computer Science and Information System (Formerly known)
ID Code:23022
Deposited By: Kamariah Mohamed Jong
Deposited On:09 Mar 2012 03:58
Last Modified:09 Mar 2012 03:58

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