A new framework for optimal classifier design
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| Format: | article |
| Jezik: | engleski |
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2013
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| Online pristup: | https://hdl.handle.net/20.500.12008/41757 https://doi.org/10.1016/j.patcog.2013.01.006 |
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| _version_ | 1868889992811511808 |
|---|---|
| author | Di Martino, Matías |
| author2 | Hernández, Guzmán Fiori, Marcelo Fernández, Alicia |
| author2_role | author author author |
| author_browse | Di Martino, Matías Fernández, Alicia Fiori, Marcelo Hernández, Guzmán |
| author_facet | Di Martino, Matías Hernández, Guzmán Fiori, Marcelo Fernández, Alicia |
| author_role | author |
| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Di Martino, Matías Hernández, Guzmán Fiori, Marcelo Fernández, Alicia |
| dc.date.none.fl_str_mv | 2013 2023-12-11T19:57:37Z 2023-12-11T19:57:37Z 20231211 |
| dc.identifier.none.fl_str_mv | Di Martino,M, Hernández,G, Fiori, M, Fernández, A. "A new framework for optimal classifier design" Pattern Recognition, 2013, v. 46, no. 8, pp. 2249-2255. https://doi.org/10.1016/j.patcog.2013.01.006. 0031-3203 https://hdl.handle.net/20.500.12008/41757 https://doi.org/10.1016/j.patcog.2013.01.006 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.relation.none.fl_str_mv | Pattern Recognition, 2013, v.46, no. 8 |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Class imbalance One class SVM F-measure Recall Precision Fraud detection Level set method Procesamiento de Señales |
| dc.title.none.fl_str_mv | A new framework for optimal classifier design |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Postprint |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_2b430f33fb57b7a2ce23f8eaf653abba |
| identifier_str_mv | Di Martino,M, Hernández,G, Fiori, M, Fernández, A. "A new framework for optimal classifier design" Pattern Recognition, 2013, v. 46, no. 8, pp. 2249-2255. https://doi.org/10.1016/j.patcog.2013.01.006. 0031-3203 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41757 |
| publishDate | 2013 |
| publishDateSort | 2013 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | A new framework for optimal classifier designDi Martino, MatíasHernández, GuzmánFiori, MarceloFernández, AliciaClass imbalanceOne class SVMF-measureRecallPrecisionFraud detectionLevel set methodProcesamiento de SeñalesPostprintThe use of alternative measures to evaluate classifier performance is gaining attention, specially for imbalanced problems. However, the use of these measures in the classifier design process is still unsolved. In this work we propose a classifier designed specifically to optimize one of these alternative measures, namely, the so-called F-measure. Nevertheless, the technique is general, and it can be used to optimize other evaluation measures. An algorithm to train the novel classifier is proposed, and the numerical scheme is tested with several databases, showing the optimality and robustness of the presented classifier.Elsevier2023-12-11T19:57:37Z2023-12-11T19:57:37Z201320231211Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionDi Martino,M, Hernández,G, Fiori, M, Fernández, A. "A new framework for optimal classifier design" Pattern Recognition, 2013, v. 46, no. 8, pp. 2249-2255. https://doi.org/10.1016/j.patcog.2013.01.006.0031-3203https://hdl.handle.net/20.500.12008/41757https://doi.org/10.1016/j.patcog.2013.01.006reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengPattern Recognition, 2013, v.46, no. 8Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)oai:colibri.udelar.edu.uy:20.500.12008/417572026-04-14T10:15:44Z |
| spellingShingle | A new framework for optimal classifier design Di Martino, Matías Class imbalance One class SVM F-measure Recall Precision Fraud detection Level set method Procesamiento de Señales |
| status_str | publishedVersion |
| title | A new framework for optimal classifier design |
| title_full | A new framework for optimal classifier design |
| title_fullStr | A new framework for optimal classifier design |
| title_full_unstemmed | A new framework for optimal classifier design |
| title_short | A new framework for optimal classifier design |
| title_sort | A new framework for optimal classifier design |
| topic | Class imbalance One class SVM F-measure Recall Precision Fraud detection Level set method Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/41757 https://doi.org/10.1016/j.patcog.2013.01.006 |