Optimizing feature selection through binary charged system search

Página do item simplificado
dc.contributor.authorRodrigues, Douglas [UNESP]
dc.contributor.authorPereira, Luis A. M. [UNESP]
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.authorRamos, Caio C. O. [UNESP]
dc.contributor.authorSouza, Andre N.
dc.contributor.authorPapa, Luciene P.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionFaculdade Sudoeste Paulista
dc.date.accessioned2014-05-27T11:30:45Z
dc.date.available2014-05-27T11:30:45Z
dc.date.issued2013-09-26
dc.description.abstractFeature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be inviable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the Charged System Search (CSS), which has never been applied to this context so far. The wrapper approach combines the power of exploration of CSS together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in four public datasets have demonstrated the validity of the proposed approach can outperform some well-known swarm-based techniques. © 2013 Springer-Verlag.en
dc.description.affiliationUNESP - Univ. Estadual Paulista Department of Computing, Bauru
dc.description.affiliationUNESP - Univ. Estadual Paulista Depart. of Electrical Engineering, Bauru
dc.description.affiliationUniversity of São Paulo Polytechnic School, São Paulo
dc.description.affiliationFaculdade Sudoeste Paulista Department of Health, Avaré
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Department of Computing, Bauru
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Depart. of Electrical Engineering, Bauru
dc.format.extent377-384
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-40261-6_45
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8047 LNCS, n. PART 1, p. 377-384, 2013.
dc.identifier.doi10.1007/978-3-642-40261-6_45
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.lattes8212775960494686
dc.identifier.scopus2-s2.0-84884491505
dc.identifier.urihttp://hdl.handle.net/11449/76647
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectCharged System Search
dc.subjectEvolutionary Optimization
dc.subjectFeature Felection
dc.subjectCharged system searches
dc.subjectEvolutionary optimizations
dc.subjectOptimization problems
dc.subjectOptimum-path forests
dc.subjectSelection techniques
dc.subjectWrapper approach
dc.subjectImage analysis
dc.subjectOptimization
dc.titleOptimizing feature selection through binary charged system searchen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights
unesp.author.lattes8212775960494686[5]
unesp.author.orcid0000-0002-8617-5404[5]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

Arquivos

Coleções

Bauru - FC - Faculdade de Ciências
Artigos - Engenharia Elétrica - FEB