An Ant Colony Optimization Based Approach for Feature Selection

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dc.contributor.author Al-Ani, Ahmed en_US
dc.contributor.editor Aboshosha; A.Dr rer.nat en_US
dc.date.accessioned 2010-05-18T06:50:22Z
dc.date.available 2010-05-18T06:50:22Z
dc.date.issued 2005 en_US
dc.identifier 2005002283 en_US
dc.identifier.citation Al-Ani Ahmed 2005, 'An Ant Colony Optimization Based Approach for Feature Selection', The International Congress for Global Science and Technology (ICGST), Cairo, Egypt, pp. 1-6. en_US
dc.identifier.issn 1687-4846 Print en_US
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. en_US
dc.publisher The International Congress for Global Science and Technology (ICGST) en_US
dc.relation.isbasedon http://www.icgst.com/AIML05/conference/index.html en_US
dc.title An Ant Colony Optimization Based Approach for Feature Selection en_US
dc.parent AIML 2005 Proceedings en_US
dc.journal.volume en_US
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.location Cairo, Egypt en_US
dc.for 100400 en_US
dc.personcode 040052 en_US
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.location.activity Cairo, Egypt en_US
dc.description.keywords Feature selection, Ant colony optimization, Ant system, Pattern recognition. en_US
dc.staffid 040052 en_US


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