A new framework for optimal classifier design
Resumen:
The 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.
| 2013 | |
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Class imbalance One class SVM F-measure Recall Precision Fraud detection Level set method Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://hdl.handle.net/20.500.12008/41757
https://doi.org/10.1016/j.patcog.2013.01.006 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522938602127360 |
|---|---|
| author | Di Martino, Matías |
| author2 | Hernández, Guzmán Fiori, Marcelo Fernández, Alicia |
| author2_role | author author author |
| author_facet | Di Martino, Matías Hernández, Guzmán Fiori, Marcelo Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Di Martino, Matías Hernández, Guzmán Fiori, Marcelo Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:37Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:37Z |
| dc.date.issued.es.fl_str_mv | 2013 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.none.fl_txt_mv | The 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. |
| dc.description.es.fl_txt_mv | Postprint |
| dc.identifier.citation.es.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. |
| dc.identifier.doi.es.fl_str_mv | https://doi.org/10.1016/j.patcog.2013.01.006 |
| dc.identifier.issn.es.fl_str_mv | 0031-3203 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41757 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Elsevier |
| dc.relation.ispartof.es.fl_str_mv | Pattern Recognition, 2013, v.46, no. 8 |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Class imbalance One class SVM F-measure Recall Precision Fraud detection Level set method |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | A new framework for optimal classifier design |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Postprint |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_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 | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41757 |
| publishDate | 2013 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-12-11T19:57:37Z2023-12-11T19:57:37Z201320231211Di 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.2013.01.006PostprintThe 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.Made available in DSpace on 2023-12-11T19:57:37Z (GMT). No. of bitstreams: 5 DHFF13.pdf: 501093 bytes, checksum: 8ea4e978748fc64dda337af6b45b0aa7 (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2013enengElsevierPattern 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)Class imbalanceOne class SVMF-measureRecallPrecisionFraud detectionLevel set methodProcesamiento de SeñalesA new framework for optimal classifier designArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaDi Martino, MatíasHernández, GuzmánFiori, MarceloFernández, AliciaProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| 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 |
