Two Step Swarm Intelligence to Solve the Feature Selection Problem

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dc.creatorGómez,Yudel
dc.creatorBello,Rafael
dc.creatorPuris,Amilkar
dc.creatorGarcía,María
dc.creatorNowe,Ann
dc.date2008
dc.date.accessioned2024-02-06T12:56:55Z
dc.date.available2024-02-06T12:56:55Z
dc.descriptionIn this paper we propose a new approach to Swarm Intelligence called Two-Step Swarm Intelligence. The basic idea is to split the heuristic search performed by agents into two stages. In the first step the agents build partial solutions which, are used as initial states in the second step. We have studied the performance of this new approach for the Feature Selection Problem by using Ant Colony Optimization and Particle Swarm Optimization. The feature selection is based on the reduct concept of the Rough Set Theory. Experimental results obtained show that Two-step approach improves the performance of ACO and PSO metaheuristics when calculating reducts in terms of computation time cost and the quality of reducts.
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dc.identifierhttps://doi.org/10.3217/jucs-014-15-2582
dc.identifierhttps://lib.jucs.org/article/29170/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/9851
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 14(15): 2582-2596
dc.subjectFeature Selection Problem
dc.subjectSwarm Intelligence
dc.subjectAnt Colony Optimization
dc.subjectParticle Swarm Optimization
dc.subjectRough Set Theory
dc.subjectTwo-Step Swarm Intelligence
dc.titleTwo Step Swarm Intelligence to Solve the Feature Selection Problem
dc.typeResearch Article
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