A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application

MLP is a model of artificial neural network, which is simple yet successfully applied in various applications. The instability of MLP performance where small changes in training parameter could produce different models that inhibiting attainment of high accuracy in classification applications. In th...

Full description

Saved in:

Bibliographic Details
Main Author:	Talib, Hafizah
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/46107/1/Hafizah%20Binti%20Talib24.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.46107
record_format	uketd_dc
spelling	my-usm-ep.461072020-02-06T06:07:32Z A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application 2014-02 Talib, Hafizah TK1-9971 Electrical engineering. Electronics. Nuclear engineering MLP is a model of artificial neural network, which is simple yet successfully applied in various applications. The instability of MLP performance where small changes in training parameter could produce different models that inhibiting attainment of high accuracy in classification applications. In this research, an integrated system of Multi-Layer Perceptron Ensemble (MLPE) consisting of an MLPE and a new voting algorithm has been developed to increase classification accuracy and reduce the number of reject class cases. MLPE is produced from singular MLPs that are diverse in term of training algorithm and their initial weights. Three training algorithms used are Levenberg-Marquardt (LM), Resilient Backpropagation (RP) and Bayesian Regularization (BR). In order to choose the final output of MLPE, a new voting algorithm named Trust-Sum Voting (TSV) is proposed. The effectiveness of MLPE with TSV (MLPE-TSV) has been tested on four classification case studies which are Electrical Capacitance Tomography (ECT), Landsat Satellite Image (LSI), German Credit (GC) and Pima Indian Diabetes (PID). The performance of MLPE-TSV has been compared with the performance of MLPE which employs existing voting algorithms which are Majority Voting (MLPE-MV) and Trust Voting (MLPE-TV). The obtained results have shown that the proposed MLPE-TSV is capable of increasing the accuracy of classification as compared to singular MLPs, MLPE-MV and MLPE-TV. MLPE-TSV has also managed to reduce the number of cases in reject class. 2014-02 Thesis http://eprints.usm.my/46107/ http://eprints.usm.my/46107/1/Hafizah%20Binti%20Talib24.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Talib, Hafizah A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application
description	MLP is a model of artificial neural network, which is simple yet successfully applied in various applications. The instability of MLP performance where small changes in training parameter could produce different models that inhibiting attainment of high accuracy in classification applications. In this research, an integrated system of Multi-Layer Perceptron Ensemble (MLPE) consisting of an MLPE and a new voting algorithm has been developed to increase classification accuracy and reduce the number of reject class cases. MLPE is produced from singular MLPs that are diverse in term of training algorithm and their initial weights. Three training algorithms used are Levenberg-Marquardt (LM), Resilient Backpropagation (RP) and Bayesian Regularization (BR). In order to choose the final output of MLPE, a new voting algorithm named Trust-Sum Voting (TSV) is proposed. The effectiveness of MLPE with TSV (MLPE-TSV) has been tested on four classification case studies which are Electrical Capacitance Tomography (ECT), Landsat Satellite Image (LSI), German Credit (GC) and Pima Indian Diabetes (PID). The performance of MLPE-TSV has been compared with the performance of MLPE which employs existing voting algorithms which are Majority Voting (MLPE-MV) and Trust Voting (MLPE-TV). The obtained results have shown that the proposed MLPE-TSV is capable of increasing the accuracy of classification as compared to singular MLPs, MLPE-MV and MLPE-TV. MLPE-TSV has also managed to reduce the number of cases in reject class.
format	Thesis
qualification_level	Master's degree
author	Talib, Hafizah
author_facet	Talib, Hafizah
author_sort	Talib, Hafizah
title	A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application
title_short	A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application
title_full	A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application
title_fullStr	A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application
title_full_unstemmed	A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application
title_sort	voting technique of multilayer perceptron ensemble for classification application
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2014
url	http://eprints.usm.my/46107/1/Hafizah%20Binti%20Talib24.pdf
_version_	1747821611884478464

A Voting Technique Of Multilayer Perceptron Ensemble For Classification Application

Similar Items