Optimal and linear F-measure classifiers applied to non-technical losses detection
Resumen:
Non-technical loss detection represents a very high cost to power supply companies. Finding classifiers that can deal with this problem is not easy as they have to face a high imbalance scenario with noisy data. In this paper we propose to use Optimal F-measure Classifier (OFC) and Linear F-measure Classifier (LFC), two novel algorithms that are designed to work in problems with unbalanced classes. We compare both algorithm performances with other previously used methods to solve automatic fraud detection problem.
| 2015 | |
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Class imbalance One class SVM F-measure Fraud detection Level set method Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42684 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522994025660416 |
|---|---|
| author | Rodríguez, Fernanda |
| author2 | Di Martino, Matías Kosut, Juan Pablo Santomauro, Fernando Lecumberry, Federico Fernández, Alicia |
| author2_role | author author author author author |
| author_facet | Rodríguez, Fernanda Di Martino, Matías Kosut, Juan Pablo Santomauro, Fernando Lecumberry, Federico Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Rodríguez, Fernanda Di Martino, Matías Kosut, Juan Pablo Santomauro, Fernando Lecumberry, Federico Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2024-02-26T19:52:37Z |
| dc.date.available.none.fl_str_mv | 2024-02-26T19:52:37Z |
| dc.date.issued.es.fl_str_mv | 2015 |
| dc.date.submitted.es.fl_str_mv | 20240223 |
| dc.description.abstract.none.fl_txt_mv | Non-technical loss detection represents a very high cost to power supply companies. Finding classifiers that can deal with this problem is not easy as they have to face a high imbalance scenario with noisy data. In this paper we propose to use Optimal F-measure Classifier (OFC) and Linear F-measure Classifier (LFC), two novel algorithms that are designed to work in problems with unbalanced classes. We compare both algorithm performances with other previously used methods to solve automatic fraud detection problem. |
| dc.identifier.citation.es.fl_str_mv | Rodriguez, F., Di Martino, M., Kosut, J.P., Santomauro, F., Lecumberry, F., Fernández, A "Optimal and linear f-measure classifiers applied to non-technical losses detection". Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Scienc, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_11 |
| dc.identifier.doi.es.fl_str_mv | 10.1007/978-3-319-25751-8 11 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/42684 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Springer International Publishing |
| dc.relation.ispartof.es.fl_str_mv | 20th Iberoamerican Congress, CIARP 2015, Montevideo, Uruguay, 9-12 nov, 2015 |
| 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 Fraud detection Level set method |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| 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 | Non-technical loss detection represents a very high cost to power supply companies. Finding classifiers that can deal with this problem is not easy as they have to face a high imbalance scenario with noisy data. In this paper we propose to use Optimal F-measure Classifier (OFC) and Linear F-measure Classifier (LFC), two novel algorithms that are designed to work in problems with unbalanced classes. We compare both algorithm performances with other previously used methods to solve automatic fraud detection problem. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_1ff0b6826d5c15f0d630973b52f43c2f |
| identifier_str_mv | Rodriguez, F., Di Martino, M., Kosut, J.P., Santomauro, F., Lecumberry, F., Fernández, A "Optimal and linear f-measure classifiers applied to non-technical losses detection". Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Scienc, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_11 10.1007/978-3-319-25751-8 11 |
| 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:20.500.12008/42684 |
| publishDate | 2015 |
| 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 | 2024-02-26T19:52:37Z2024-02-26T19:52:37Z201520240223Rodriguez, F., Di Martino, M., Kosut, J.P., Santomauro, F., Lecumberry, F., Fernández, A "Optimal and linear f-measure classifiers applied to non-technical losses detection". Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Scienc, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_11https://hdl.handle.net/20.500.12008/4268410.1007/978-3-319-25751-8 11Non-technical loss detection represents a very high cost to power supply companies. Finding classifiers that can deal with this problem is not easy as they have to face a high imbalance scenario with noisy data. In this paper we propose to use Optimal F-measure Classifier (OFC) and Linear F-measure Classifier (LFC), two novel algorithms that are designed to work in problems with unbalanced classes. We compare both algorithm performances with other previously used methods to solve automatic fraud detection problem.Made available in DSpace on 2024-02-26T19:52:37Z (GMT). No. of bitstreams: 5 RDKSLF15.pdf: 201717 bytes, checksum: f60a99ede5ee8e255d423c28f89b69b2 (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: 2015enengSpringer International Publishing20th Iberoamerican Congress, CIARP 2015, Montevideo, Uruguay, 9-12 nov, 2015Las 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-measureFraud detectionLevel set methodProcesamiento de SeñalesOptimal and linear F-measure classifiers applied to non-technical losses detectionPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaRodríguez, FernandaDi Martino, MatíasKosut, Juan PabloSantomauro, FernandoLecumberry, FedericoFernández, AliciaProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Optimal and linear F-measure classifiers applied to non-technical losses detection Rodríguez, Fernanda Class imbalance One class SVM F-measure Fraud detection Level set method Procesamiento de Señales |
| status_str | publishedVersion |
| title | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| title_full | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| title_fullStr | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| title_full_unstemmed | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| title_short | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| title_sort | Optimal and linear F-measure classifiers applied to non-technical losses detection |
| topic | Class imbalance One class SVM F-measure Fraud detection Level set method Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/42684 |
