New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets

Abstract

The task of classification with imbalanced datasets have attracted quite interest from researchers in the last years. The reason behind this fact is that many applications and real problems present this feature, causing standard learning algorithms not reaching the expected performance. Accordingly, many approaches have been designed to address this problem from different perspectives, i.e., data preprocessing, algorithmic modification, and cost-sensitive learning. The extension of the former techniques to ensembles of classifiers has shown to be very effective in terms of quality of the output models. However, the optimal value for the number of classifiers in the pool cannot be known a priori, which can alter the behaviour of the system. For this reason, ordering-based pruning techniques have been proposed to address this issue in standard classifier learning problems. The hitch is that those metrics are not designed specifically for imbalanced classification, thus hindering the performance in this context. In this work, we propose two novel adaptations for ordering-based pruning metrics in imbalanced classification, specifically the margin distance minimization and the boosting-based approach. Throughout a complete experimental study, our analysis shows the goodness of both schemes in contrast with the unpruned ensembles and the standard pruning metrics in Bagging-based ensembles.