Diversity Regularized Ensemble Pruning

Diversity among individual classifiers is recognized to play a key role in ensemble, however, few theoretical properties are known for classification. In this paper, by focusing on the popular ensemble pruning setting (i.e., combining classifier by voting and measuring diversity in pairwise manner), we present a theoretical study on the effect of diversity on the generalization performance of voting in the PAC-learning framework. It is disclosed that the diversity is closely-related to the hypothesis space complexity, and encouraging diversity can be regarded to apply regularization on ensemble methods. Guided by this analysis, we apply explicit diversity regularization to ensemble pruning, and propose the Diversity Regularized Ensemble Pruning (DREP) method. Experimental results show the effectiveness of DREP.