Feature selection with simple ANN ensembles

Autores
Izetta Riera, C. Javier; Granitto, Pablo Miguel
Año de publicación
2009
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Feature selection is a well-known pre-processing technique, commonly used with high-dimensional datasets. Its main goal is to discard useless or redundant variables, reducing the dimensionality of the input space, in order to increase the performance and interpretability of models. In this work we introduce the ANN-RFE, a new technique for feature selection that combines the accurate and time-e cient RFE method with the strong discrimination capabilities of ANN ensembles. In particular, we discuss two feature importance metrics that can be used with ANN-RFE: the shu ing and dE metrics. We evaluate the new method using an arti cial example and ve real-world wide datasets, including gene-expression data. Our results suggest that both metrics have equivalent capabilities for the selection of informative variables. ANNRFE seems to produce overall results that are equivalent to previous e cient methods, but can be more accurate on particular datasets.
Presentado en el X Workshop Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Process metrics
feature selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/20884

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spelling Feature selection with simple ANN ensemblesIzetta Riera, C. JavierGranitto, Pablo MiguelCiencias InformáticasProcess metricsfeature selectionFeature selection is a well-known pre-processing technique, commonly used with high-dimensional datasets. Its main goal is to discard useless or redundant variables, reducing the dimensionality of the input space, in order to increase the performance and interpretability of models. In this work we introduce the ANN-RFE, a new technique for feature selection that combines the accurate and time-e cient RFE method with the strong discrimination capabilities of ANN ensembles. In particular, we discuss two feature importance metrics that can be used with ANN-RFE: the shu ing and dE metrics. We evaluate the new method using an arti cial example and ve real-world wide datasets, including gene-expression data. Our results suggest that both metrics have equivalent capabilities for the selection of informative variables. ANNRFE seems to produce overall results that are equivalent to previous e cient methods, but can be more accurate on particular datasets.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI)2009-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf60-69http://sedici.unlp.edu.ar/handle/10915/20884enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:25Zoai:sedici.unlp.edu.ar:10915/20884Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:25.851SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Feature selection with simple ANN ensembles
title Feature selection with simple ANN ensembles
spellingShingle Feature selection with simple ANN ensembles
Izetta Riera, C. Javier
Ciencias Informáticas
Process metrics
feature selection
title_short Feature selection with simple ANN ensembles
title_full Feature selection with simple ANN ensembles
title_fullStr Feature selection with simple ANN ensembles
title_full_unstemmed Feature selection with simple ANN ensembles
title_sort Feature selection with simple ANN ensembles
dc.creator.none.fl_str_mv Izetta Riera, C. Javier
Granitto, Pablo Miguel
author Izetta Riera, C. Javier
author_facet Izetta Riera, C. Javier
Granitto, Pablo Miguel
author_role author
author2 Granitto, Pablo Miguel
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Process metrics
feature selection
topic Ciencias Informáticas
Process metrics
feature selection
dc.description.none.fl_txt_mv Feature selection is a well-known pre-processing technique, commonly used with high-dimensional datasets. Its main goal is to discard useless or redundant variables, reducing the dimensionality of the input space, in order to increase the performance and interpretability of models. In this work we introduce the ANN-RFE, a new technique for feature selection that combines the accurate and time-e cient RFE method with the strong discrimination capabilities of ANN ensembles. In particular, we discuss two feature importance metrics that can be used with ANN-RFE: the shu ing and dE metrics. We evaluate the new method using an arti cial example and ve real-world wide datasets, including gene-expression data. Our results suggest that both metrics have equivalent capabilities for the selection of informative variables. ANNRFE seems to produce overall results that are equivalent to previous e cient methods, but can be more accurate on particular datasets.
Presentado en el X Workshop Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description Feature selection is a well-known pre-processing technique, commonly used with high-dimensional datasets. Its main goal is to discard useless or redundant variables, reducing the dimensionality of the input space, in order to increase the performance and interpretability of models. In this work we introduce the ANN-RFE, a new technique for feature selection that combines the accurate and time-e cient RFE method with the strong discrimination capabilities of ANN ensembles. In particular, we discuss two feature importance metrics that can be used with ANN-RFE: the shu ing and dE metrics. We evaluate the new method using an arti cial example and ve real-world wide datasets, including gene-expression data. Our results suggest that both metrics have equivalent capabilities for the selection of informative variables. ANNRFE seems to produce overall results that are equivalent to previous e cient methods, but can be more accurate on particular datasets.
publishDate 2009
dc.date.none.fl_str_mv 2009-10
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dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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