B-ROC Curves for the Assessment of Classifiers over Imbalanced Data Sets
A. Cardenas and J. S. Baras
2006 Nectar Track of the Twenty First National Conference on Artificial Intelligence, Boston, Massachusetts, July 16-20, 2006
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. This presentation is based on section 4 of (C´ardenas, Baras, & Seamon 2006).