Partial AUC maximization in a linear combination of dichotomizers

Classifier combination is a useful and common methodology to design an effective classification system. A large number of combination rules has been proposed hitherto, mostly aimed at minimizing the error rate. Recently, some methods have been presented that are devoted to maximize the area under the ROC curve (AUC), a more suitable performance measure when dealing with two-class problems with imprecise environment and/or imbalanced class priors. However, there are several applications that do not operate in the complete range of the ROC curve, but only in particular regions of it. In these cases, it is better to analyze the performance only in a part of the curve and to use the partial AUC (pAUC). This paper presents a new method that aims at maximizing the pAUC by means of linear combination of classifiers. The effectiveness of the proposed method has been proved on two biometric databases.

► Linear combination of two-class classifiers through non-parametric pAUC maximization.
► Algorithm for combining two classifiers for a given maximum FPR.
► Greedy approach to combine more classifiers for a given maximum FPR.
► pAUC maximization not equivalent to AUC maximization.
► Very good performance of our method compared with other approaches.