Comparison of multi-objective algorithms applied to feature selection

Detalhes bibliográficos
Autor(a) principal: Türkşen, Özlem
Data de Publicação: 2013
Outros Autores: Vieira, Susana M., Madeira, JFA, Apaydin, Aysen, Sousa, João M. C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.21/6984
Resumo: The feature selection problem can be formulated as a multi-objective optimization (MOO) problem, as it involves the minimization of the feature subset cardinality and the misclassification error. In this chapter, a comparison of MOO algorithms applied to feature selection is presented. The used MOO methods are: Nondominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi Objective Simulated Annealing (AMOSA), and Direct Multi Search (DMS). To test the feature subset solutions, Takagi- Sugeno fuzzy models are used as classifiers. To solve the feature selection problem, AMOSA was adapted to deal with discrete optimization. The multi-objective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed.
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spelling Comparison of multi-objective algorithms applied to feature selectionThe feature selection problem can be formulated as a multi-objective optimization (MOO) problem, as it involves the minimization of the feature subset cardinality and the misclassification error. In this chapter, a comparison of MOO algorithms applied to feature selection is presented. The used MOO methods are: Nondominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi Objective Simulated Annealing (AMOSA), and Direct Multi Search (DMS). To test the feature subset solutions, Takagi- Sugeno fuzzy models are used as classifiers. To solve the feature selection problem, AMOSA was adapted to deal with discrete optimization. The multi-objective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed.Springer VerlagRCIPLTürkşen, ÖzlemVieira, Susana M.Madeira, JFAApaydin, AysenSousa, João M. C.2017-05-08T09:09:46Z20132013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/6984engTÜRKSEN, Özlem; [et al] – Comparison of multi-objective algorithms applied to feature selection. Studies in Fuzziness and Soft Computing. ISSN 1434-9922. Vol. 285, (2013), pp. 359-375.1434-992210.1007/978-3-642-30278-7_28metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T09:52:27ZPortal AgregadorONG
dc.title.none.fl_str_mv Comparison of multi-objective algorithms applied to feature selection
title Comparison of multi-objective algorithms applied to feature selection
spellingShingle Comparison of multi-objective algorithms applied to feature selection
Türkşen, Özlem
title_short Comparison of multi-objective algorithms applied to feature selection
title_full Comparison of multi-objective algorithms applied to feature selection
title_fullStr Comparison of multi-objective algorithms applied to feature selection
title_full_unstemmed Comparison of multi-objective algorithms applied to feature selection
title_sort Comparison of multi-objective algorithms applied to feature selection
author Türkşen, Özlem
author_facet Türkşen, Özlem
Vieira, Susana M.
Madeira, JFA
Apaydin, Aysen
Sousa, João M. C.
author_role author
author2 Vieira, Susana M.
Madeira, JFA
Apaydin, Aysen
Sousa, João M. C.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Türkşen, Özlem
Vieira, Susana M.
Madeira, JFA
Apaydin, Aysen
Sousa, João M. C.
description The feature selection problem can be formulated as a multi-objective optimization (MOO) problem, as it involves the minimization of the feature subset cardinality and the misclassification error. In this chapter, a comparison of MOO algorithms applied to feature selection is presented. The used MOO methods are: Nondominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi Objective Simulated Annealing (AMOSA), and Direct Multi Search (DMS). To test the feature subset solutions, Takagi- Sugeno fuzzy models are used as classifiers. To solve the feature selection problem, AMOSA was adapted to deal with discrete optimization. The multi-objective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed.
publishDate 2013
dc.date.none.fl_str_mv 2013
2013-01-01T00:00:00Z
2017-05-08T09:09:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.21/6984
url http://hdl.handle.net/10400.21/6984
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv TÜRKSEN, Özlem; [et al] – Comparison of multi-objective algorithms applied to feature selection. Studies in Fuzziness and Soft Computing. ISSN 1434-9922. Vol. 285, (2013), pp. 359-375.
1434-9922
10.1007/978-3-642-30278-7_28
dc.rights.driver.fl_str_mv metadata only access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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