A rough sets based pruning method for bagging ensemble

Ensemble techniques train a set of component classifiers and then combine their predictions to classify new pat-terns. Bagging is one of the most popular ensemble techniques for improving weak classifiers. However, it is hard to deployin many real applications because of the large memory requirement and high computation cost to store and vote the predic- tions of component classifiers. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise informa- tion, which has attracted a lot of attention from theory and application fields. In this paper, a novel rough sets based meth-od is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation. Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.