Semisupervised approach to non technical losses detection
Resumen:
Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling.
| 2014 | |
|
Electricity fraud Support vector machine Semisupervised approach SVMlight TSVM Unbalance class problem Procesamiento de Señales |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
|
https://hdl.handle.net/20.500.12008/41831
https://doi.org/10.1007/978-3-319-12568-8_85 |
|
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940013510656 |
|---|---|
| author | Tacón, Juan |
| author2 | Melgarejo, Damián Rodríguez, Fernanda Lecumberry, Federico Fernández, Alicia |
| author2_role | author author author author |
| author_facet | Tacón, Juan Melgarejo, Damián Rodríguez, Fernanda Lecumberry, Federico Fernández, Alicia |
| author_role | author |
| bitstream.checksum.fl_str_mv | 528b6a3c8c7d0c6e28129d576e989607 9833653f73f7853880c94a6fead477b1 4afdbb8c545fd630ea7db775da747b2f 9da0b6dfac957114c6a7714714b86306 bf1a2ad7f7a914d32a95cadec84076d2 |
| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
| bitstream.url.fl_str_mv | http://localhost:8080/xmlui/bitstream/20.500.12008/41831/5/license.txt http://localhost:8080/xmlui/bitstream/20.500.12008/41831/2/license_text http://localhost:8080/xmlui/bitstream/20.500.12008/41831/3/license_url http://localhost:8080/xmlui/bitstream/20.500.12008/41831/4/license_rdf http://localhost:8080/xmlui/bitstream/20.500.12008/41831/1/TMRLF14.pdf |
| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Tacón, Juan Melgarejo, Damián Rodríguez, Fernanda Lecumberry, Federico Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2023-12-11T19:57:58Z |
| dc.date.available.none.fl_str_mv | 2023-12-11T19:57:58Z |
| dc.date.issued.es.fl_str_mv | 2014 |
| dc.date.submitted.es.fl_str_mv | 20231211 |
| dc.description.abstract.none.fl_txt_mv | Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling. |
| dc.identifier.citation.es.fl_str_mv | Tacón, J, Melgarejo, D, Rodríguez, F, Lecumberry, F, Fernández, A. "Semisupervised approach to non technical losses detection". Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_85 |
| dc.identifier.doi.es.fl_str_mv | https://doi.org/10.1007/978-3-319-12568-8_85 |
| dc.identifier.isbn.es.fl_str_mv | 978-3-319-12568-8 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41831 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Springer |
| dc.relation.ispartof.es.fl_str_mv | Bayro-Corrochano E., Hancock E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. |
| 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 | Electricity fraud Support vector machine Semisupervised approach SVMlight TSVM Unbalance class problem |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Semisupervised approach to non technical losses detection |
| dc.type.es.fl_str_mv | Capítulo de libro |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/bookPart |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling. |
| eu_rights_str_mv | openAccess |
| format | bookPart |
| id | COLIBRI_0214843c72b1473a675a1395de81ed6a |
| identifier_str_mv | Tacón, J, Melgarejo, D, Rodríguez, F, Lecumberry, F, Fernández, A. "Semisupervised approach to non technical losses detection". Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_85 978-3-319-12568-8 |
| 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:20.500.12008/41831 |
| publishDate | 2014 |
| 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 | 2023-12-11T19:57:58Z2023-12-11T19:57:58Z201420231211Tacón, J, Melgarejo, D, Rodríguez, F, Lecumberry, F, Fernández, A. "Semisupervised approach to non technical losses detection". Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_85978-3-319-12568-8https://hdl.handle.net/20.500.12008/41831https://doi.org/10.1007/978-3-319-12568-8_85Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the F measure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling.Made available in DSpace on 2023-12-11T19:57:58Z (GMT). No. of bitstreams: 5 TMRLF14.pdf: 368189 bytes, checksum: bf1a2ad7f7a914d32a95cadec84076d2 (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: 2014enengSpringerBayro-Corrochano E., Hancock E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827.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)Electricity fraudSupport vector machineSemisupervised approachSVMlightTSVMUnbalance class problemProcesamiento de SeñalesSemisupervised approach to non technical losses detectionCapítulo de libroinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaTacón, JuanMelgarejo, DamiánRodríguez, FernandaLecumberry, FedericoFernández, AliciaLICENSElicense.txttext/plain4244http://localhost:8080/xmlui/bitstream/20.500.12008/41831/5/license.txt528b6a3c8c7d0c6e28129d576e989607MD55CC-LICENSElicense_textapplication/octet-stream21936http://localhost:8080/xmlui/bitstream/20.500.12008/41831/2/license_text9833653f73f7853880c94a6fead477b1MD52license_urlapplication/octet-stream49http://localhost:8080/xmlui/bitstream/20.500.12008/41831/3/license_url4afdbb8c545fd630ea7db775da747b2fMD53license_rdfapplication/octet-stream23148http://localhost:8080/xmlui/bitstream/20.500.12008/41831/4/license_rdf9da0b6dfac957114c6a7714714b86306MD54ORIGINALTMRLF14.pdfapplication/pdf368189http://localhost:8080/xmlui/bitstream/20.500.12008/41831/1/TMRLF14.pdfbf1a2ad7f7a914d32a95cadec84076d2MD5120.500.12008/418312023-12-11 16:57:58.183oai:colibri.udelar.edu.uy:20.500.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Universidadhttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestmabel.seroubian@seciu.edu.uyUruguayopendoar:47712024-07-25T14:33:42.901483COLIBRI - Universidad de la Repúblicafalse |
| spellingShingle | Semisupervised approach to non technical losses detection Tacón, Juan Electricity fraud Support vector machine Semisupervised approach SVMlight TSVM Unbalance class problem Procesamiento de Señales |
| status_str | publishedVersion |
| title | Semisupervised approach to non technical losses detection |
| title_full | Semisupervised approach to non technical losses detection |
| title_fullStr | Semisupervised approach to non technical losses detection |
| title_full_unstemmed | Semisupervised approach to non technical losses detection |
| title_short | Semisupervised approach to non technical losses detection |
| title_sort | Semisupervised approach to non technical losses detection |
| topic | Electricity fraud Support vector machine Semisupervised approach SVMlight TSVM Unbalance class problem Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/41831 https://doi.org/10.1007/978-3-319-12568-8_85 |
