Statistical learning approaches for discriminant features selection
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of the Brazilian Computer Society |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002008000200002 |
Resumo: | Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed. |
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Journal of the Brazilian Computer Society |
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Statistical learning approaches for discriminant features selectionSupervised statistical learningDiscriminant features selectionSeparating hyperplanesSupervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed.Sociedade Brasileira de Computação2008-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002008000200002Journal of the Brazilian Computer Society v.14 n.2 2008reponame:Journal of the Brazilian Computer Societyinstname:Sociedade Brasileira de Computação (SBC)instacron:UFRGS10.1007/BF03192556info:eu-repo/semantics/openAccessGiraldi,Gilson A.Rodrigues,Paulo S.Kitani,Edson C.Sato,João R.Thomaz,Carlos E.eng2008-10-24T00:00:00Zoai:scielo:S0104-65002008000200002Revistahttps://journal-bcs.springeropen.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpjbcs@icmc.sc.usp.br1678-48040104-6500opendoar:2008-10-24T00:00Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC)false |
dc.title.none.fl_str_mv |
Statistical learning approaches for discriminant features selection |
title |
Statistical learning approaches for discriminant features selection |
spellingShingle |
Statistical learning approaches for discriminant features selection Giraldi,Gilson A. Supervised statistical learning Discriminant features selection Separating hyperplanes |
title_short |
Statistical learning approaches for discriminant features selection |
title_full |
Statistical learning approaches for discriminant features selection |
title_fullStr |
Statistical learning approaches for discriminant features selection |
title_full_unstemmed |
Statistical learning approaches for discriminant features selection |
title_sort |
Statistical learning approaches for discriminant features selection |
author |
Giraldi,Gilson A. |
author_facet |
Giraldi,Gilson A. Rodrigues,Paulo S. Kitani,Edson C. Sato,João R. Thomaz,Carlos E. |
author_role |
author |
author2 |
Rodrigues,Paulo S. Kitani,Edson C. Sato,João R. Thomaz,Carlos E. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Giraldi,Gilson A. Rodrigues,Paulo S. Kitani,Edson C. Sato,João R. Thomaz,Carlos E. |
dc.subject.por.fl_str_mv |
Supervised statistical learning Discriminant features selection Separating hyperplanes |
topic |
Supervised statistical learning Discriminant features selection Separating hyperplanes |
description |
Supervised statistical learning covers important models like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we describe the idea of using the discriminant weights given by SVM and LDA separating hyperplanes to select the most discriminant features to separate sample groups. Our method, called here as Discriminant Feature Analysis (DFA), is not restricted to any particular probability density function and the number of meaningful discriminant features is not limited to the number of groups. To evaluate the discriminant features selected, two case studies have been investigated using face images and breast lesion data sets. In both case studies, our experimental results show that the DFA approach provides an intuitive interpretation of the differences between the groups, highlighting and reconstructing the most important statistical changes between the sample groups analyzed. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002008000200002 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002008000200002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/BF03192556 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Computação |
publisher.none.fl_str_mv |
Sociedade Brasileira de Computação |
dc.source.none.fl_str_mv |
Journal of the Brazilian Computer Society v.14 n.2 2008 reponame:Journal of the Brazilian Computer Society instname:Sociedade Brasileira de Computação (SBC) instacron:UFRGS |
instname_str |
Sociedade Brasileira de Computação (SBC) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Journal of the Brazilian Computer Society |
collection |
Journal of the Brazilian Computer Society |
repository.name.fl_str_mv |
Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC) |
repository.mail.fl_str_mv |
jbcs@icmc.sc.usp.br |
_version_ |
1754734669966868480 |