| 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.contributor.author | GIRALDI, G. A. | |
| dc.contributor.author | RODRIGUES, Paulo | |
| dc.contributor.author | KITANI, E. C. | |
| dc.contributor.author | SATO, J. R. | |
| dc.contributor.author | THOMAZ, C. E. | |
| dc.date.accessioned | 2019-08-17T20:00:28Z | |
| dc.date.available | 2019-08-17T20:00:28Z | |
| dc.date.issued | 2008 | |
| dc.identifier.citation | GIRALDI, 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.issn | 0104-6500 | |
| dc.identifier.uri | https://repositorio.fei.edu.br/handle/FEI/979 | |
| dc.relation.ispartof | Journal of the Brazilian Computer Society | |
| dc.rights | Acesso Aberto | |
| dc.title | Statistical learning approaches for discriminant features selection | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.identifier.doi | 10.1007/BF03192556 | |
| dc.description.volume | 14 | |
| dc.description.issuenumber | 2 | |
| dc.description.firstpage | 7 | |
| dc.description.lastpage | 22 | |
| dc.subject.otherlanguage | Supervised statistical learning | en |
| dc.subject.otherlanguage | Discriminant features selection | en |
| dc.subject.otherlanguage | Separating hyperplanes | en |
| fei.scopus.citations | 9 | |
| fei.scopus.eid | 2-s2.0-55349148369 | |
| fei.scopus.updated | 2024-11-01 | |
| dc.identifier.url | http://dx.doi.org/10.1007/BF03192556 | |
| dc.description.abstractalternative | 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 | en |
