@inproceedings{4c17dff4ff454206b05e004e0526b77a,
title = "Optimal and linear F-measure classifiers applied to non-technical losses detection",
abstract = "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.",
keywords = "Class imbalance, F-measure, Fraud detection, Level set method, One class SVM, Precision, Recall",
author = "Fernanda Rodriguez and Martino, \{Mat{\'i}as Di\} and Kosut, \{Juan Pablo\} and Fernando Santomauro and Federico Lecumberry and Alicia Fern{\'a}ndez",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-25751-8\_11",
language = "Ingl{\'e}s",
isbn = "9783319257501",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "83--91",
editor = "Alvaro Pardo and Josef Kittler",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}