Feature selection on wide multiclass problems using OVA-RFE
- Autores
- Granitto, Pablo Miguel; Burgos, Andrés
- Año de publicación
- 2009
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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.
Fil: Granitto, Pablo Miguel. Erasmus Université Paul Cézanne Aix Marseille III; Francia. Universidad Nacional de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Burgos, Andrés. Erasmus Université Paul Cézanne Aix Marseille III; Francia. Universidad Nacional de Rosario; Argentina - Materia
-
FEATURE SELECTION
MULTICLASS
ONE-VS-ALL
WIDE DATASETS - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/145380
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:11:52Zoai: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:34982025-09-29 10:11:52.336CONICET 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 |
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 |
topic |
FEATURE SELECTION MULTICLASS ONE-VS-ALL WIDE DATASETS |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
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. Fil: Granitto, Pablo Miguel. Erasmus Université Paul Cézanne Aix Marseille III; Francia. Universidad Nacional de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Burgos, Andrés. Erasmus Université Paul Cézanne Aix Marseille III; Francia. Universidad Nacional de Rosario; Argentina |
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/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Sociedad Iberoamericana de Inteligencia Artificial |
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Sociedad Iberoamericana de Inteligencia Artificial |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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