AUC-based combination of dichotomizers: is whole maximization also effective for partial maximization?

The combination of classifiers is an established technique to improve the classification performance. When dealing with two-class classification problems, a frequently used performance measure is the Area under the ROC curve (AUC) since it is more effective than accuracy. However, in many applications, like medical or biometric ones, tests with false positive rate over a given value are of no practical use and thus irrelevant for evaluating the performance of the system. In these cases, the performance should be measured by looking only at the interesting part of the ROC curve. Consequently, the optimization goal is to maximize only a part of the AUC instead of the whole area. In this paper we propose a method tailored for these situations which builds a linear combination of two dichotomizers maximizing the partial AUC (pAUC). Another aim of the paper is to understand if methods that maximize the AUC can maximize also the pAUC. An empirical comparison drawn between algorithms maximizing the AUC and the proposed method shows that this latter is more effective for the pAUC maximization than methods designed to globally optimize the AUC