@inproceedings{8cd32fa27cd64b5f9e60d540cfb25c13,
title = "Improving electric fraud detection using class imbalance strategies",
abstract = "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.",
keywords = "Combining classifier, Electricity theft, Optimum path forest, Support vector machine, Unbalance class problem, UTE",
author = "\{Di Martino\}, Ma{\'t}ias and Federico Decia and Juan Molinelli and Alicia Fer{\'n}andez",
year = "2012",
language = "Ingl{\'e}s",
isbn = "9789898425980",
series = "ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods",
pages = "135--141",
booktitle = "ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods",
note = "1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012 ; Conference date: 06-02-2012 Through 08-02-2012",
}