Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach o...

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Main Author:	Alwan, Hiba Basim
Format:	Thesis
Language:	eng eng
Published:	2013
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/4419/1/s92846.pdf https://etd.uum.edu.my/4419/7/s92846_abstract.pdf
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id	my-uum-etd.4419
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku-Mahamud, Ku Ruhana
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Alwan, Hiba Basim Hybrid ACO and SVM algorithm for pattern classification
description	Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Alwan, Hiba Basim
author_facet	Alwan, Hiba Basim
author_sort	Alwan, Hiba Basim
title	Hybrid ACO and SVM algorithm for pattern classification
title_short	Hybrid ACO and SVM algorithm for pattern classification
title_full	Hybrid ACO and SVM algorithm for pattern classification
title_fullStr	Hybrid ACO and SVM algorithm for pattern classification
title_full_unstemmed	Hybrid ACO and SVM algorithm for pattern classification
title_sort	hybrid aco and svm algorithm for pattern classification
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2013
url	https://etd.uum.edu.my/4419/1/s92846.pdf https://etd.uum.edu.my/4419/7/s92846_abstract.pdf
_version_	1776103643366293504
spelling	my-uum-etd.44192023-01-25T01:06:21Z Hybrid ACO and SVM algorithm for pattern classification 2013 Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO. 2013 Thesis https://etd.uum.edu.my/4419/ https://etd.uum.edu.my/4419/1/s92846.pdf text eng public https://etd.uum.edu.my/4419/7/s92846_abstract.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abd-Alsabour, N. & Randall, M. (2010). Feature selection for classification using an ant colony system. Proceedings of The 6th International Conference on e-Science Workshops, 7-10 December 2010, Brisbane, QLD, 86-91. Abd-Alsabour, N. (2010). Feature selection for classification using an ant system approach. In M. Hinchey, B. Kleinjohann, L. Kleinjohann, P. Lindsay, F. Ramming, J. Timmis & M. 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