Exploring margin for dynamic ensemble selection

Abstract

How to effectively combine the outputs of base classifiers is one of the key issues in ensemble learning. A new dynamic ensemble selection algorithm is proposed in this paper. In order to predict a sample, the base classifiers whose classification confidences on this sample are greater than or equal to specified threshold value are selected. Since margin is an important factor to the generalization performance of voting classifiers, thus the threshold value is estimated via the minimization of margin loss. We analyze the proposed algorithm in detail and compare it with some other multiple classifiers fusion algorithms. The experimental results validate the effectiveness of our algorithm. © 2013 Springer-Verlag.