Feature selection on wide multiclass problems using OVA-RFE

Feature selection is a pre–processing technique commonly used with high–dimensional datasets. It is aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. For multiclass classification p...

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Detalhes bibliográficos
Autores: Granitto, Pablo Miguel, Burgos, Andrés
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2009
País:Argentina
Recursos:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositório:CONICET Digital (CONICET)
Idioma:inglês
OAI Identifier:oai:ri.conicet.gov.ar:11336/145380
Acesso em linha:http://hdl.handle.net/11336/145380
Access Level:Acceso aberto
Palavra-chave:FEATURE SELECTION
MULTICLASS
ONE-VS-ALL
WIDE DATASETS
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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spelling Feature selection on wide multiclass problems using OVA-RFEGranitto, Pablo MiguelBurgos, AndrésFEATURE SELECTIONMULTICLASSONE-VS-ALLWIDE DATASETShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Feature selection is a pre–processing technique commonly used with high–dimensional datasets. It is aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. For multiclass classification problems, recent works suggested that decomposing the multiclass problem in a set of binary ones, and doing the feature selection on the binary problems could be a sound strategy. In this work we combined the well–known Recursive Feature Elimination (RFE) algorithm with the simple One–Vs–All (OVA) technique for multiclass problems, to produce the new OVA–RFE selection method. We evaluated OVA–RFE using wide datasets from genomic and mass– spectrometry analysis, and several classifiers. In particular, we compared the new method with the traditional RFE (applied to a direct multiclass classifier) in terms of accuracy and stability. Our results show that OVA– RFE is no better than the traditional method, which is in opposition to previous results on similar methods. The opposite results are related to a different interpretation of the real number of variables in use by both methods.Fil: Granitto, Pablo Miguel. Erasmus Université Paul Cézanne Aix Marseille III; Francia. Universidad Nacional de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Burgos, Andrés. Erasmus Université Paul Cézanne Aix Marseille III; Francia. Universidad Nacional de Rosario; ArgentinaSociedad Iberoamericana de Inteligencia Artificial2009-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/145380Granitto, Pablo Miguel; Burgos, Andrés; Feature selection on wide multiclass problems using OVA-RFE; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 13; 44; 12-2009; 27-341137-36011988-3064CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.4114/ia.v13i44.1043info:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.html#2009info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T14:02:28Zoai:ri.conicet.gov.ar:11336/145380instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982024-05-08 14:02:29.191CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Feature selection on wide multiclass problems using OVA-RFE
title Feature selection on wide multiclass problems using OVA-RFE
spellingShingle Feature selection on wide multiclass problems using OVA-RFE
Granitto, Pablo Miguel
FEATURE SELECTION
MULTICLASS
ONE-VS-ALL
WIDE DATASETS
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
title_short Feature selection on wide multiclass problems using OVA-RFE
title_full Feature selection on wide multiclass problems using OVA-RFE
title_fullStr Feature selection on wide multiclass problems using OVA-RFE
title_full_unstemmed Feature selection on wide multiclass problems using OVA-RFE
title_sort Feature selection on wide multiclass problems using OVA-RFE
dc.creator.none.fl_str_mv Granitto, Pablo Miguel
Burgos, Andrés
author Granitto, Pablo Miguel
author_facet Granitto, Pablo Miguel
Burgos, Andrés
author_role author
author2 Burgos, Andrés
author2_role author
dc.subject.none.fl_str_mv FEATURE SELECTION
MULTICLASS
ONE-VS-ALL
WIDE DATASETS
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
topic FEATURE SELECTION
MULTICLASS
ONE-VS-ALL
WIDE DATASETS
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
description Feature selection is a pre–processing technique commonly used with high–dimensional datasets. It is aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. For multiclass classification problems, recent works suggested that decomposing the multiclass problem in a set of binary ones, and doing the feature selection on the binary problems could be a sound strategy. In this work we combined the well–known Recursive Feature Elimination (RFE) algorithm with the simple One–Vs–All (OVA) technique for multiclass problems, to produce the new OVA–RFE selection method. We evaluated OVA–RFE using wide datasets from genomic and mass– spectrometry analysis, and several classifiers. In particular, we compared the new method with the traditional RFE (applied to a direct multiclass classifier) in terms of accuracy and stability. Our results show that OVA– RFE is no better than the traditional method, which is in opposition to previous results on similar methods. The opposite results are related to a different interpretation of the real number of variables in use by both methods.
publishDate 2009
dc.date.none.fl_str_mv 2009-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/145380
Granitto, Pablo Miguel; Burgos, Andrés; Feature selection on wide multiclass problems using OVA-RFE; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 13; 44; 12-2009; 27-34
1137-3601
1988-3064
CONICET Digital
CONICET
url http://hdl.handle.net/11336/145380
identifier_str_mv Granitto, Pablo Miguel; Burgos, Andrés; Feature selection on wide multiclass problems using OVA-RFE; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 13; 44; 12-2009; 27-34
1137-3601
1988-3064
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.4114/ia.v13i44.1043
info:eu-repo/semantics/altIdentifier/url/http://journal.iberamia.org/public/Vol.1-14.html#2009
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 15.228081

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