AUC dominant unsupervised ensemble of binary classifiers

Abstract

Ensemble methods are widely used in practice with the hope of obtaining better predictive performance than could be obtained from any of the constituent classifiers in the ensemble. Most of the existing literature is concerned with learning ensembles in a supervised setting. In this paper we propose an unsupervised iterative algorithm to combine the discriminant scores from different binary classifiers. We prove that (under certain assumptions) the Area Under the ROC Curve (AUC) of the resulting ensemble is greater than or equal to the AUC of the best classifier (with maximum AUC). We also experimentally validate this claim on a number of datasets and also show that the performance is better than the supervised ensembles.