Abstract
Class-imbalance is very common in real world. However, traditional state-of-the-art classifiers do not work well on imbalanced data sets for imbalanced class distribution. This paper considers imbalance learning from the viewpoint of ensemble pruning, and proposes a novel approach called IBEP (Instance-Based Ensemble Pruning) to improve classifier's performance on these data sets. Unlike traditional approaches which consider imbalance problem in training stage, IBEP focuses on the problem in prediction stage. Given an unlabeled instance, IBEP tries to search for the k nearest neighbors as the corresponding pruning set and adopts ensemble pruning strategy to select a subset of ensemble members to form sub-ensemble based on the pruning set to predict the instance. In this way, IBEP pays more attention to rare class and achieves better performance on imbalanced data set. Besides, two widely used sampling techniques, under-sampling and SMOTE, are skillfully combined with IBEP to further improve its performance. Experimental results on 14 data sets show that IBEP performs significantly better than many state-of-the-art classification methods on all metrics used in this paper including recall, f$-measure and g-mean.