Feature Subset Selection Using Ant Colony Optimization

Al-Ani, A

Feature Subset Selection Using Ant Colony Optimization

Al-Ani, A

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Publisher:: International Journal of Computational Intelligence (IJCI)
Publication Type:: Journal Article
Citation:: Al-Ani Ahmed 2005, 'Feature Subset Selection Using Ant Colony Optimization', International Journal of Computational Intelligence (IJCI), vol. 2, no. 1, pp. 53-58.
Issue Date:: 2005

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Field	Value	Language
dc.contributor.author	Al-Ani, A	en_US
dc.date.accessioned	2010-05-14T07:44:39Z
dc.date.available	2010-05-14T07:44:39Z
dc.date.created	2010-05-14T07:44:39Z	en_US
dc.date.issued	2005
dc.identifier	2005002282	en_US
dc.identifier.citation	Al-Ani Ahmed 2005, 'Feature Subset Selection Using Ant Colony Optimization', International Journal of Computational Intelligence (IJCI), vol. 2, no. 1, pp. 53-58.	en_US
dc.identifier.issn	1304-4508	en_US
dc.identifier.other	C1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6181
dc.description.abstract	Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.	en_US
dc.publisher	International Journal of Computational Intelligence (IJCI)	en_US
dc.relation.ispartof	International Journal of Computational Intelligence	en_US
dc.relation.hasversion	Accepted manuscript version	en_US
dc.relation.isbasedon	http://www.enformatika.org/journals/1304-2386/	en_US
dc.title	Feature Subset Selection Using Ant Colony Optimization
dc.type	Journal article
utslib.citation.volume	2	en_US
utslib.citation.number	1	en_US
utslib.location	Turkey	en_US
utslib.citation.startpage	53	en_US
utslib.citation.endpage	58	en_US
utslib.identifier.org	Health Technologies	en_US
utslib.for	090399	en_US
utslib.copyright.status	open_access

Abstract:

Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.

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http://hdl.handle.net/10453/6181