An unsupervised approach to feature discretization and selection
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/8569 |
Resumo: | Many learning problems require handling high dimensional data sets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevante (oreven detrimental) for the learning tasks. It ist hus clear that the reisaneed for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for médium and high-dimensional datas ets. The experimental results on several standard data sets, with both sparse and dense features, showthe efficiency of the proposed techniques as well as improvements over previous related techniques. |
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An unsupervised approach to feature discretization and selectionFeature discretizationFeature quantizationFeature selectionLinde–Buzo–Gray algorithmSparse dataSupport vectormachinesNaïve BayesK-nearest neighborMany learning problems require handling high dimensional data sets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevante (oreven detrimental) for the learning tasks. It ist hus clear that the reisaneed for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for médium and high-dimensional datas ets. The experimental results on several standard data sets, with both sparse and dense features, showthe efficiency of the proposed techniques as well as improvements over previous related techniques.ElsevierRCIPLJ. Ferreira, ArturFigueiredo, Mário A. T.2018-06-06T08:56:51Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/8569engFERREIRA, Artur Jorge; FIGUEIREDO, Mário A. T. – An unsupervised approach to feature discretization and selection. Pattern Recognition. ISSN 0031-3203. Vol. 45, (2012), pp. 3048-3060.0031-3203metadata 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:RCAAP2023-08-03T09:56:09Zoai:repositorio.ipl.pt:10400.21/8569Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:17:18.629534Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An unsupervised approach to feature discretization and selection |
title |
An unsupervised approach to feature discretization and selection |
spellingShingle |
An unsupervised approach to feature discretization and selection J. Ferreira, Artur Feature discretization Feature quantization Feature selection Linde–Buzo–Gray algorithm Sparse data Support vectormachines Naïve Bayes K-nearest neighbor |
title_short |
An unsupervised approach to feature discretization and selection |
title_full |
An unsupervised approach to feature discretization and selection |
title_fullStr |
An unsupervised approach to feature discretization and selection |
title_full_unstemmed |
An unsupervised approach to feature discretization and selection |
title_sort |
An unsupervised approach to feature discretization and selection |
author |
J. Ferreira, Artur |
author_facet |
J. Ferreira, Artur Figueiredo, Mário A. T. |
author_role |
author |
author2 |
Figueiredo, Mário A. T. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
J. Ferreira, Artur Figueiredo, Mário A. T. |
dc.subject.por.fl_str_mv |
Feature discretization Feature quantization Feature selection Linde–Buzo–Gray algorithm Sparse data Support vectormachines Naïve Bayes K-nearest neighbor |
topic |
Feature discretization Feature quantization Feature selection Linde–Buzo–Gray algorithm Sparse data Support vectormachines Naïve Bayes K-nearest neighbor |
description |
Many learning problems require handling high dimensional data sets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevante (oreven detrimental) for the learning tasks. It ist hus clear that the reisaneed for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for médium and high-dimensional datas ets. The experimental results on several standard data sets, with both sparse and dense features, showthe efficiency of the proposed techniques as well as improvements over previous related techniques. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 2012-01-01T00:00:00Z 2018-06-06T08:56:51Z |
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/10400.21/8569 |
url |
http://hdl.handle.net/10400.21/8569 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
FERREIRA, Artur Jorge; FIGUEIREDO, Mário A. T. – An unsupervised approach to feature discretization and selection. Pattern Recognition. ISSN 0031-3203. Vol. 45, (2012), pp. 3048-3060. 0031-3203 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133435170652160 |