Enhanced self organizing map with particle swarm optimization for classification problems

Hybridization 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 hybrid architectures have weaknesses such as slow convergence time; always being trap...

Full description

Saved in:

Bibliographic Details
Main Author:	Hasan, Shafaatunnur
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/16396/7/ShafaatunnurHasanMFSKSM2010.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.16396
record_format	uketd_dc
spelling	my-utm-ep.163962017-09-17T08:31:12Z Enhanced self organizing map with particle swarm optimization for classification problems 2010 Hasan, Shafaatunnur QA75 Electronic computers. Computer science Hybridization 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 hybrid architectures have weaknesses such as slow convergence time; always being trapped in the local minima and others. This study proposes a hybridization method by improving the Self Organizing Map (SOM) Lattice Structure with Particle Swarm Optimization (ESOMPSO) for solving 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 improvement of the SOM lattice structure using the proposed Enhanced SOM is implemented by optimizing the weights using PSO to obtain better output quality. The process is done in two stages: the first stage is conducted by training the weights using the Enhanced SOM, and the second stage is implemented by optimizing these weights with the PSO. The proposed method has been tested on various standard datasets. The comparisons are done on standard SOM, Enhanced SOM (ESOM), SOMPSO and ESOMPSO using various distance measurements. The performance of the proposed method is validated using classification accuracy and quantization error. The experiments have shown that ESOMPSO yields promising result with better average accuracy and quantization errors. 2010 Thesis http://eprints.utm.my/id/eprint/16396/ http://eprints.utm.my/id/eprint/16396/7/ShafaatunnurHasanMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Hasan, Shafaatunnur Enhanced self organizing map with particle swarm optimization for classification problems
description	Hybridization 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 hybrid architectures have weaknesses such as slow convergence time; always being trapped in the local minima and others. This study proposes a hybridization method by improving the Self Organizing Map (SOM) Lattice Structure with Particle Swarm Optimization (ESOMPSO) for solving 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 improvement of the SOM lattice structure using the proposed Enhanced SOM is implemented by optimizing the weights using PSO to obtain better output quality. The process is done in two stages: the first stage is conducted by training the weights using the Enhanced SOM, and the second stage is implemented by optimizing these weights with the PSO. The proposed method has been tested on various standard datasets. The comparisons are done on standard SOM, Enhanced SOM (ESOM), SOMPSO and ESOMPSO using various distance measurements. The performance of the proposed method is validated using classification accuracy and quantization error. The experiments have shown that ESOMPSO yields promising result with better average accuracy and quantization errors.
format	Thesis
qualification_level	Master's degree
author	Hasan, Shafaatunnur
author_facet	Hasan, Shafaatunnur
author_sort	Hasan, Shafaatunnur
title	Enhanced self organizing map with particle swarm optimization for classification problems
title_short	Enhanced self organizing map with particle swarm optimization for classification problems
title_full	Enhanced self organizing map with particle swarm optimization for classification problems
title_fullStr	Enhanced self organizing map with particle swarm optimization for classification problems
title_full_unstemmed	Enhanced self organizing map with particle swarm optimization for classification problems
title_sort	enhanced self organizing map with particle swarm optimization for classification problems
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/16396/7/ShafaatunnurHasanMFSKSM2010.pdf
_version_	1747815033508724736

Enhanced self organizing map with particle swarm optimization for classification problems

Similar Items