Cover Image

Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolution...

Full description

Saved in:
Bibliographic Details
Main Authors: Ibrahim, Ashraf Osman, Shamsuddin, Siti Mariyam, Qasem, Sultan Noman
Format: Article
Language:English
Published: Universiti Utara Malaysia Press 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:https://repo.uum.edu.my/id/eprint/30412/1/JICT%2014%2000%202015%2021-38.pdf
https://repo.uum.edu.my/id/eprint/30412/
https://e-journal.uum.edu.my/index.php/jict/article/view/8154
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.30412
record_format eprints
spelling my.uum.repo.304122024-02-14T14:53:07Z https://repo.uum.edu.my/id/eprint/30412/ Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems Ibrahim, Ashraf Osman Shamsuddin, Siti Mariyam Qasem, Sultan Noman QA75 Electronic computers. Computer science Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature. Universiti Utara Malaysia Press 2015 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/30412/1/JICT%2014%2000%202015%2021-38.pdf Ibrahim, Ashraf Osman and Shamsuddin, Siti Mariyam and Qasem, Sultan Noman (2015) Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems. Journal of Information and Communication Technology, 14. pp. 21-38. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/8154
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ibrahim, Ashraf Osman
Shamsuddin, Siti Mariyam
Qasem, Sultan Noman
Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
description Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.
format Article
author Ibrahim, Ashraf Osman
Shamsuddin, Siti Mariyam
Qasem, Sultan Noman
author_facet Ibrahim, Ashraf Osman
Shamsuddin, Siti Mariyam
Qasem, Sultan Noman
author_sort Ibrahim, Ashraf Osman
title Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
title_short Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
title_full Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
title_fullStr Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
title_full_unstemmed Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
title_sort hybrid nsga-ii optimization for improving the three-term bp network for multiclass classification problems
publisher Universiti Utara Malaysia Press
publishDate 2015
url https://repo.uum.edu.my/id/eprint/30412/1/JICT%2014%2000%202015%2021-38.pdf
https://repo.uum.edu.my/id/eprint/30412/
https://e-journal.uum.edu.my/index.php/jict/article/view/8154
_version_ 1792158591686279168
score 13.211869

Similar Items