Abstract：Theoretical and experimental results indicate that for the ensemble classifiers with the same training error the one with higher margin distribution on training examples has better generalization performance. Therefore,the concept of margins of examples is introduced to ensemble pruning and it is employed to supervise the design of ensemble pruning methods. Based on the margins,a new metric called margin based metric (MBM) is designed to evaluate the importance of a classifier to an ensemble and an example set,and then a greedy ensemble pruning method called MBM based ensemble selection is proposed to reduce the ensemble size and improve its accuracy. The experimental results on 30 UCI datasets show that compared with other state of the art greedy ensemble pruning methods,the ensembles selected by the proposed method have better performance.
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