Comparing different labeling strategies in anomalous power consumptions detection
Resumen:
Detecting anomalous events is a complex task, specially when it should be performed manually and for several hours. In the case of electrical power consumptions, the detection of non-technical losses also has a high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detecting the largest number of frauds. This work analyses the performance of a strategy based on learning from expert labeling: suspect/no-suspect, with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for imbalance problems, improves performance in terms of the Fmeasure with inspection labels, avoiding hours of experts labeling.
| 2015 | |
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Electricity fraud Support vector machine Optimum Path Forest Unbalance class problem Combining classifier UTE Procesamiento de Señales |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42687 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940632170496 |
|---|---|
| author | Rodríguez, Fernanda |
| author2 | Lecumberry, Federico Fernández, Alicia |
| author2_role | author author |
| author_facet | Rodríguez, Fernanda Lecumberry, Federico Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Rodríguez, Fernanda Lecumberry, Federico Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2024-02-26T19:52:38Z |
| dc.date.available.none.fl_str_mv | 2024-02-26T19:52:38Z |
| dc.date.issued.es.fl_str_mv | 2015 |
| dc.date.submitted.es.fl_str_mv | 20240223 |
| dc.description.abstract.none.fl_txt_mv | Detecting anomalous events is a complex task, specially when it should be performed manually and for several hours. In the case of electrical power consumptions, the detection of non-technical losses also has a high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detecting the largest number of frauds. This work analyses the performance of a strategy based on learning from expert labeling: suspect/no-suspect, with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for imbalance problems, improves performance in terms of the Fmeasure with inspection labels, avoiding hours of experts labeling. |
| dc.description.es.fl_txt_mv | Trabajo presentado en nternational Conference on Pattern Recognition Applications and Methods, 2014 |
| dc.identifier.citation.es.fl_str_mv | Rodríguez, F, Lecumberry, F, Fernández, A. "Comparing different labeling strategies in anomalous power consumptions detection". Publicado en: Fred, A., De Marsico, M., Tabbone, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2014. Lecture Notes in Computer Science, v. 9443. Springer, Cham. https://doi.org/10.1007/978-3-319-25530-9_13 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/42687 |
| 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 | Electricity fraud Support vector machine Optimum Path Forest Unbalance class problem Combining classifier UTE |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Comparing different labeling strategies in anomalous power consumptions detection |
| dc.type.es.fl_str_mv | Ponencia |
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| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Trabajo presentado en nternational Conference on Pattern Recognition Applications and Methods, 2014 |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_1321b4293e4324c0fa88e58caed9dd57 |
| identifier_str_mv | Rodríguez, F, Lecumberry, F, Fernández, A. "Comparing different labeling strategies in anomalous power consumptions detection". Publicado en: Fred, A., De Marsico, M., Tabbone, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2014. Lecture Notes in Computer Science, v. 9443. Springer, Cham. https://doi.org/10.1007/978-3-319-25530-9_13 |
| 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:20.500.12008/42687 |
| 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 | 2024-02-26T19:52:38Z2024-02-26T19:52:38Z201520240223Rodríguez, F, Lecumberry, F, Fernández, A. "Comparing different labeling strategies in anomalous power consumptions detection". Publicado en: Fred, A., De Marsico, M., Tabbone, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2014. Lecture Notes in Computer Science, v. 9443. Springer, Cham. https://doi.org/10.1007/978-3-319-25530-9_13https://hdl.handle.net/20.500.12008/42687Trabajo presentado en nternational Conference on Pattern Recognition Applications and Methods, 2014Detecting anomalous events is a complex task, specially when it should be performed manually and for several hours. In the case of electrical power consumptions, the detection of non-technical losses also has a high economic impact. The diversity and big number of consumption records, makes it very important to find an efficient automatic method for detecting the largest number of frauds. This work analyses the performance of a strategy based on learning from expert labeling: suspect/no-suspect, with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for imbalance problems, improves performance in terms of the Fmeasure with inspection labels, avoiding hours of experts labeling.Made available in DSpace on 2024-02-26T19:52:38Z (GMT). No. of bitstreams: 5 RLF15.pdf: 805482 bytes, checksum: c2e3e86260179533f1c9a37d0eeb1f9a (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: 2015enengLas 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 machineOptimum PathForestUnbalance class problemCombining classifierUTEProcesamiento de SeñalesComparing different labeling strategies in anomalous power consumptions detectionPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaRodríguez, FernandaLecumberry, FedericoFernández, AliciaProcesamiento de SeñalesTratamiento de 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- Universidad de la Repúblicafalse |
| spellingShingle | Comparing different labeling strategies in anomalous power consumptions detection Rodríguez, Fernanda Electricity fraud Support vector machine Optimum Path Forest Unbalance class problem Combining classifier UTE Procesamiento de Señales |
| status_str | publishedVersion |
| title | Comparing different labeling strategies in anomalous power consumptions detection |
| title_full | Comparing different labeling strategies in anomalous power consumptions detection |
| title_fullStr | Comparing different labeling strategies in anomalous power consumptions detection |
| title_full_unstemmed | Comparing different labeling strategies in anomalous power consumptions detection |
| title_short | Comparing different labeling strategies in anomalous power consumptions detection |
| title_sort | Comparing different labeling strategies in anomalous power consumptions detection |
| topic | Electricity fraud Support vector machine Optimum Path Forest Unbalance class problem Combining classifier UTE Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/42687 |
