Título:
Optimal and linear F-measure classifiers applied to non-technical losses detection
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Autor:
Rodríguez, Fernanda
Di Martino, Matías
Kosut, Juan Pablo
Santomauro, Fernando
Lecumberry, Federico
Fernández, Alicia
Di Martino, Matías
Kosut, Juan Pablo
Santomauro, Fernando
Lecumberry, Federico
Fernández, Alicia
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Tipo de documento:
Ponencia
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Palabras clave:
Class imbalance
One class SVM
F-measure
Fraud detection
Level set method
One class SVM
F-measure
Fraud detection
Level set method
Descriptores:
Procesamiento de Señales
Año de publicación:
2015
Contenido:
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.
Descripción:
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Springer International Publishing
EN:
20th Iberoamerican Congress, CIARP 2015, Montevideo, Uruguay, 9-12 nov, 2015
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Citación:
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
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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 | ||
|---|---|---|---|---|---|
| RDKSLF15.pdf | — | 196.99 KB | Adobe PDF |