Optimization of the Area under the ROC Curve

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.