Título:
Improving electric fraud detection using class imbalance strategies
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Autor:
Di Martino, Matías
Decia, Federico
Molinelli, Juan
Fernández, Alicia
Decia, Federico
Molinelli, Juan
Fernández, Alicia
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Tipo de documento:
Ponencia
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Año de publicación:
2012
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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.
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International Conference on Pattern Recognition Applications and Methods (IPRAM 2012)
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Citación:
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
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| DDMF12.pdf | — | 665.12 KB | Adobe PDF |