Improving electric fraud detection using class imbalance strategies
Resumen:
Improving nontechnical loss detection is a huge challenge for electric companies. The great number of clients and the diversity of the different types of fraud makes this a very complex task. In this paper we present a fraud detection strategy based on class imbalance research. An automatic detection tool combining classification strategies is proposed. Individual classifiers such as One Class SVM, Cost Sensitive SVM (CS-SVM), Optimum Path Forest (OPF) and C4.5 Tree, and combination functions are designed taken special care in the data s class imbalance nature. Analysis over consumers historical kWh load profile data from Uruguayan Electric Company (UTE) shows that using combination and balancing techniques improves automatic detection performance.
| 2012 | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41146 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522937929990144 |
|---|---|
| author | Di Martino, Matías |
| author2 | Decia, Federico Molinelli, Juan Fernández, Alicia |
| author2_role | author author author |
| author_facet | Di Martino, Matías Decia, Federico Molinelli, Juan Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Di Martino, Matías Decia, Federico Molinelli, Juan Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2023-11-14T17:04:31Z |
| dc.date.available.none.fl_str_mv | 2023-11-14T17:04:31Z |
| dc.date.issued.es.fl_str_mv | 2012 |
| dc.date.submitted.es.fl_str_mv | 20231114 |
| dc.description.abstract.none.fl_txt_mv | Improving nontechnical loss detection is a huge challenge for electric companies. The great number of clients and the diversity of the different types of fraud makes this a very complex task. In this paper we present a fraud detection strategy based on class imbalance research. An automatic detection tool combining classification strategies is proposed. Individual classifiers such as One Class SVM, Cost Sensitive SVM (CS-SVM), Optimum Path Forest (OPF) and C4.5 Tree, and combination functions are designed taken special care in the data s class imbalance nature. Analysis over consumers historical kWh load profile data from Uruguayan Electric Company (UTE) shows that using combination and balancing techniques improves automatic detection performance. |
| dc.identifier.citation.es.fl_str_mv | Di Martino, M, Decia, F, Molinelli, J, Fernández, A. "Improving electric fraud detection using class imbalance strategies" International Conference on Pattern Recognition Applications and Methods. Vilamoura, Portugal, 5-8 feb. 2012 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41146 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.relation.ispartof.es.fl_str_mv | International Conference on Pattern Recognition Applications and Methods (IPRAM 2012) |
| 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.title.none.fl_str_mv | Improving electric fraud detection using class imbalance strategies |
| 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 | Improving nontechnical loss detection is a huge challenge for electric companies. The great number of clients and the diversity of the different types of fraud makes this a very complex task. In this paper we present a fraud detection strategy based on class imbalance research. An automatic detection tool combining classification strategies is proposed. Individual classifiers such as One Class SVM, Cost Sensitive SVM (CS-SVM), Optimum Path Forest (OPF) and C4.5 Tree, and combination functions are designed taken special care in the data s class imbalance nature. Analysis over consumers historical kWh load profile data from Uruguayan Electric Company (UTE) shows that using combination and balancing techniques improves automatic detection performance. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_6e58dc78a9e64dce550a85bcd1b67a12 |
| identifier_str_mv | Di Martino, M, Decia, F, Molinelli, J, Fernández, A. "Improving electric fraud detection using class imbalance strategies" International Conference on Pattern Recognition Applications and Methods. Vilamoura, Portugal, 5-8 feb. 2012 |
| 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:20.500.12008/41146 |
| publishDate | 2012 |
| 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 | 2023-11-14T17:04:31Z2023-11-14T17:04:31Z201220231114Di Martino, M, Decia, F, Molinelli, J, Fernández, A. "Improving electric fraud detection using class imbalance strategies" International Conference on Pattern Recognition Applications and Methods. Vilamoura, Portugal, 5-8 feb. 2012https://hdl.handle.net/20.500.12008/41146Improving nontechnical loss detection is a huge challenge for electric companies. The great number of clients and the diversity of the different types of fraud makes this a very complex task. In this paper we present a fraud detection strategy based on class imbalance research. An automatic detection tool combining classification strategies is proposed. Individual classifiers such as One Class SVM, Cost Sensitive SVM (CS-SVM), Optimum Path Forest (OPF) and C4.5 Tree, and combination functions are designed taken special care in the data s class imbalance nature. Analysis over consumers historical kWh load profile data from Uruguayan Electric Company (UTE) shows that using combination and balancing techniques improves automatic detection performance.Made available in DSpace on 2023-11-14T17:04:31Z (GMT). No. of bitstreams: 5 DDMF12.pdf: 681079 bytes, checksum: 7b0a8ecde590faf6674b08317f4d1bcd (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4194 bytes, checksum: 7f2e2c17ef6585de66da58d1bfa8b5e1 (MD5) Previous issue date: 2012enengInternational Conference on Pattern Recognition Applications and Methods (IPRAM 2012)Las 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)Improving electric fraud detection using class imbalance strategiesPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaDi Martino, MatíasDecia, FedericoMolinelli, JuanFernández, 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- Universidad de la Repúblicafalse |
| spellingShingle | Improving electric fraud detection using class imbalance strategies Di Martino, Matías |
| status_str | publishedVersion |
| title | Improving electric fraud detection using class imbalance strategies |
| title_full | Improving electric fraud detection using class imbalance strategies |
| title_fullStr | Improving electric fraud detection using class imbalance strategies |
| title_full_unstemmed | Improving electric fraud detection using class imbalance strategies |
| title_short | Improving electric fraud detection using class imbalance strategies |
| title_sort | Improving electric fraud detection using class imbalance strategies |
| url | https://hdl.handle.net/20.500.12008/41146 |
