An optimal multiclass classifier design
Resumen:
The use of different evaluation measures for classification tasks have gained a significant amount of attention in the past decade, specially for those problems with multiple and imbalanced classes. However, the optimization of classifiers with respect to these measures is still heuristic, using ad-hoc rules with classical accuracy-optimized classifiers. We propose a classifier designed specifically to optimize one of the possible measures, namely, the so-called G-mean. Nevertheless, the technique is general, and it can be used to optimize generic evaluation measures. The optimization algorithm to train the classifier is described, and the numerical scheme is tested showing its usability and robustness. The code is publicly available, as well as the datasets used along this paper.
| 2016 | |
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Support vector machines Optimization Algorithm design and analysis Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42713 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940921577472 |
|---|---|
| author | Fiori, Marcelo |
| author2 | Di Martino, Matías Fernández, Alicia |
| author2_role | author author |
| author_facet | Fiori, Marcelo Di Martino, Matías Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Fiori, Marcelo Di Martino, Matías Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2024-02-26T19:52:45Z |
| dc.date.available.none.fl_str_mv | 2024-02-26T19:52:45Z |
| dc.date.issued.es.fl_str_mv | 2016 |
| dc.date.submitted.es.fl_str_mv | 20240223 |
| dc.description.abstract.none.fl_txt_mv | The use of different evaluation measures for classification tasks have gained a significant amount of attention in the past decade, specially for those problems with multiple and imbalanced classes. However, the optimization of classifiers with respect to these measures is still heuristic, using ad-hoc rules with classical accuracy-optimized classifiers. We propose a classifier designed specifically to optimize one of the possible measures, namely, the so-called G-mean. Nevertheless, the technique is general, and it can be used to optimize generic evaluation measures. The optimization algorithm to train the classifier is described, and the numerical scheme is tested showing its usability and robustness. The code is publicly available, as well as the datasets used along this paper. |
| dc.description.es.fl_txt_mv | Trabajo presentado en 23rd International Conference on Pattern Recognition (ICPR), Cancun, México, 4-8 dic, 2016 |
| dc.identifier.citation.es.fl_str_mv | Fiori, M, Di Martino, M, Fernández, A. "An optimal multiclass classifier design" Publicado en: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 dic, 2016, pp. 480-485, doi: 10.1109/ICPR.2016.7899680. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/42713 |
| dc.language.iso.none.fl_str_mv | en eng |
| 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 | Support vector machines Optimization Algorithm design and analysis |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | An optimal multiclass classifier design |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo presentado en 23rd International Conference on Pattern Recognition (ICPR), Cancun, México, 4-8 dic, 2016 |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_d4c65981bb43536fe49fc6f61921a58e |
| identifier_str_mv | Fiori, M, Di Martino, M, Fernández, A. "An optimal multiclass classifier design" Publicado en: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 dic, 2016, pp. 480-485, doi: 10.1109/ICPR.2016.7899680. |
| 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:20.500.12008/42713 |
| publishDate | 2016 |
| 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 | 2024-02-26T19:52:45Z2024-02-26T19:52:45Z201620240223Fiori, M, Di Martino, M, Fernández, A. "An optimal multiclass classifier design" Publicado en: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 dic, 2016, pp. 480-485, doi: 10.1109/ICPR.2016.7899680.https://hdl.handle.net/20.500.12008/42713Trabajo presentado en 23rd International Conference on Pattern Recognition (ICPR), Cancun, México, 4-8 dic, 2016The use of different evaluation measures for classification tasks have gained a significant amount of attention in the past decade, specially for those problems with multiple and imbalanced classes. However, the optimization of classifiers with respect to these measures is still heuristic, using ad-hoc rules with classical accuracy-optimized classifiers. We propose a classifier designed specifically to optimize one of the possible measures, namely, the so-called G-mean. Nevertheless, the technique is general, and it can be used to optimize generic evaluation measures. The optimization algorithm to train the classifier is described, and the numerical scheme is tested showing its usability and robustness. The code is publicly available, as well as the datasets used along this paper.Made available in DSpace on 2024-02-26T19:52:45Z (GMT). No. of bitstreams: 5 FDF16.pdf: 1458511 bytes, checksum: 0dbe653d625dae08c92f6a355d5a6cf1 (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: 2016enengLas 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)Support vector machinesOptimizationAlgorithm design and analysisProcesamiento de SeñalesAn optimal multiclass classifier designPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaFiori, MarceloDi Martino, MatíasFernández, AliciaProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | An optimal multiclass classifier design Fiori, Marcelo Support vector machines Optimization Algorithm design and analysis Procesamiento de Señales |
| status_str | publishedVersion |
| title | An optimal multiclass classifier design |
| title_full | An optimal multiclass classifier design |
| title_fullStr | An optimal multiclass classifier design |
| title_full_unstemmed | An optimal multiclass classifier design |
| title_short | An optimal multiclass classifier design |
| title_sort | An optimal multiclass classifier design |
| topic | Support vector machines Optimization Algorithm design and analysis Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/42713 |
