Optimization of the Area under the ROC Curve

Authors:
Cristiano Leite Castro;Antonio Padua Braga
Affiliations:
-;-
Venue:
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Year:
2008

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Abstract

In this paper, we propose a new binary classification algorithm (AUCtron), based on gradient descent learning, that directly optimizes AUC (Area Under the ROC Curve). We compare it with a linear classifier and with AUCsplit proposed. The AUCtron algorithm implicitly considers class prior probabilities in the decision criteria. Our results demonstrated that AUC is a sensitive enough metric that when used in small and imbalanced data sets may lead to a better separation.