Título:
Comparing different labeling strategies in anomalous power consumptions detection
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Autor:
Rodríguez, Fernanda
Lecumberry, Federico
Fernández, Alicia
Lecumberry, Federico
Fernández, Alicia
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Tipo de documento:
Ponencia
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Palabras clave:
Electricity fraud
Support vector machine
Optimum Path
Forest
Unbalance class problem
Combining classifier
UTE
Support vector machine
Optimum Path
Forest
Unbalance class problem
Combining classifier
UTE
Descriptores:
Procesamiento de Señales
Año de publicación:
2015
Contenido:
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.
Descripción:
Trabajo presentado en nternational Conference on Pattern Recognition Applications and Methods, 2014
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Citación:
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
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| RLF15.pdf | — | 786.6 KB | Adobe PDF |