An Ant Colony Optimization Based Approach for Feature Selection

Al-Ani, A

An Ant Colony Optimization Based Approach for Feature Selection

Al-Ani, A

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Publisher:: The International Congress for Global Science and Technology (ICGST)
Publication Type:: Conference Proceeding
Citation:: AIML 2005 Proceedings, 2005, pp. 1 - 6
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.editor	Aboshosha	en_US
dc.contributor.editor	nat, ADR	en_US
dc.date	2005-12-19	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	AIML 2005 Proceedings, 2005, pp. 1 - 6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7184
dc.description.abstract	This paper presents a new feature subset selection algorithm based on the Ant Colony Optimization (ACO). ACO is a metaheuristic inspired by the behaviour of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by utilizing distributed computing, local heuristics and previous knowledge. We modified the ACO algorithm so that it can be used to search for the best subsets of features. A new pheromone trail update formula is presented, and the various parameters that lead to better convergence are tested. Results on speech classification problem show that the proposed algorithm achieves better performance than both greedy and genetic algorithm based feature selection methods.	en_US
dc.publisher	The International Congress for Global Science and Technology (ICGST)	en_US
dc.relation.ispartof	AIML 2005 Proceedings	en_US
dc.relation.ispartof	ICGT International Conference on Artifical Intelligence and Machine Learning	en_US
dc.title	An Ant Colony Optimization Based Approach for Feature Selection	en_US
dc.type	Conference Proceeding
utslib.location	Cairo, Egypt	en_US
utslib.location.activity	Cairo, Egypt	en_US
utslib.for	1004 Medical Biotechnology	en_US
dc.location.activity	Cairo, Egypt	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Cairo, Egypt	en_US
pubs.start-date	2005-12-19	en_US

Abstract:

This paper presents a new feature subset selection algorithm based on the Ant Colony Optimization (ACO). ACO is a metaheuristic inspired by the behaviour of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by utilizing distributed computing, local heuristics and previous knowledge. We modified the ACO algorithm so that it can be used to search for the best subsets of features. A new pheromone trail update formula is presented, and the various parameters that lead to better convergence are tested. Results on speech classification problem show that the proposed algorithm achieves better performance than both greedy and genetic algorithm based feature selection methods.

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http://hdl.handle.net/10453/7184