Multistrategy Self-Organizing Map Learning for Classification Problems
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 convergenc...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2011/121787 |
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doaj-94a5746e9f634ee1b4a2685db218297c2020-11-24T22:51:17ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732011-01-01201110.1155/2011/121787121787Multistrategy Self-Organizing Map Learning for Classification ProblemsS. Hasan0S. M. Shamsuddin1Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Skudai, 81300 Johor, MalaysiaSoft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Skudai, 81300 Johor, MalaysiaMultistrategy 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.http://dx.doi.org/10.1155/2011/121787 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Hasan S. M. Shamsuddin |
spellingShingle |
S. Hasan S. M. Shamsuddin Multistrategy Self-Organizing Map Learning for Classification Problems Computational Intelligence and Neuroscience |
author_facet |
S. Hasan S. M. Shamsuddin |
author_sort |
S. Hasan |
title |
Multistrategy Self-Organizing Map Learning for Classification Problems |
title_short |
Multistrategy Self-Organizing Map Learning for Classification Problems |
title_full |
Multistrategy Self-Organizing Map Learning for Classification Problems |
title_fullStr |
Multistrategy Self-Organizing Map Learning for Classification Problems |
title_full_unstemmed |
Multistrategy Self-Organizing Map Learning for Classification Problems |
title_sort |
multistrategy self-organizing map learning for classification problems |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2011-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2011/121787 |
work_keys_str_mv |
AT shasan multistrategyselforganizingmaplearningforclassificationproblems AT smshamsuddin multistrategyselforganizingmaplearningforclassificationproblems |
_version_ |
1725670510235222016 |