B-ROC Curves for the Assessment of Classifiers over Imbalanced Data Sets

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 21

New Scientific and Technical Advances in Research (Nectar) Papers

The class imbalance problem appears to be ubiquitous to a large portion of the machine learning and data mining communities. One of the key questions in this setting is how to evaluate the learning algorithms in the case of class imbalances. In this paper we introduce the Bayesian Receiver Operating Characteristic (B-ROC) curves, as a set of tradeoff curves that combine in an intuitive way, the variables that are more relevant to the evaluation of classifiers over imbalanced data sets.