An Ant Colony Optimization Based Approach for Feature Selection

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dc.contributor.author Al-Ani, A
dc.contributor.editor Aboshosha
dc.contributor.editor nat, ADR
dc.date.accessioned 2010-05-18T06:50:22Z
dc.date.issued 2005-01
dc.identifier.citation AIML 2005 Proceedings, 2005, pp. 1 - 6
dc.identifier.other E1 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.
dc.publisher The International Congress for Global Science and Technology (ICGST)
dc.title An Ant Colony Optimization Based Approach for Feature Selection
dc.type Conference Proceeding
dc.parent AIML 2005 Proceedings
dc.journal.number en_US
dc.publocation Cairo, Egypt en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 6 en_US
dc.cauo.name FEIT. A/DRsch Ctre for Health Technologies en_US
dc.conference en_US
dc.conference Verified OK en_US
dc.conference ICGT International Conference on Artifical Intelligence and Machine Learning
dc.conference.location Cairo, Egypt en_US
dc.for 1004 Medical Biotechnology
dc.personcode 040052
dc.percentage 100 en_US
dc.classification.name Medical Biotechnology en_US
dc.classification.type FOR-08 en_US
dc.custom ICGT International Conference on Artifical Intelligence and Machine Learning en_US
dc.date.activity 20051219 en_US
dc.date.activity 2005-12-19
dc.location.activity Cairo, Egypt en_US
dc.description.keywords Feature selection, Ant colony optimization, Ant system, Pattern recognition. en_US
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)


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