Statistical learning approaches for discriminant features selection

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dc.contributor.authorGIRALDI, G. A.
dc.contributor.authorRODRIGUES, Paulo
dc.contributor.authorKITANI, E. C.
dc.contributor.authorSATO, J. R.
dc.contributor.authorTHOMAZ, C. E.
dc.date.accessioned2019-08-17T20:00:28Z
dc.date.available2019-08-17T20:00:28Z
dc.date.issued2008
dc.description.abstractalternativeSupervised 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 analyzeden
dc.description.firstpage7
dc.description.issuenumber2
dc.description.lastpage22
dc.description.volume14
dc.identifier.citationGIRALDI, G. A.; RODRIGUES, Paulo; KITANI, E. C.; SATO, J. R.; THOMAZ, C. E. Statistical learning approaches for discriminant features selection. Journal of the Brazilian Computer Society (Impresso), v. 14, n. 2, p. 7-22, 2008.
dc.identifier.doi10.1007/BF03192556
dc.identifier.issn0104-6500
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/979
dc.identifier.urlhttp://dx.doi.org/10.1007/BF03192556
dc.relation.ispartofJournal of the Brazilian Computer Society
dc.rightsAcesso Aberto
dc.rights.license"Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY-NC 4.0). Fonte: <https://journal-bcs.springeropen.com/submission-guidelines/copyright>. Acesso em: 05/11/2019
dc.subject.otherlanguageSupervised statistical learningen
dc.subject.otherlanguageDiscriminant features selectionen
dc.subject.otherlanguageSeparating hyperplanesen
dc.titleStatistical learning approaches for discriminant features selectionpt_BR
dc.typeArtigopt_BR
fei.scopus.citations9
fei.scopus.eid2-s2.0-55349148369
fei.scopus.updated2024-03-04
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