Combination of multiple classifier using feature space partitioning

Abstract

Combination of Multiple Classifier has been consider as the approach of improving the classification performance. The popular diversification approach named local specialization is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the sub-space. This paper presents a novel feature space partitioning algorithm for the combination of multiple classifier. The proposed method uses pairwise measure to get the diversity between classifiers and selects the complementary classifiers to get the pseudo labels. Based on the pseudo labels, it splits the feature space into constituents and selects the best classifier committee from the pool of available classifiers. The partitioning and selection are taken place simultaneously as part of a compound optimization process aimed at maximizing system performance. Evolutionary methods are used to find the optimal solution. The experimental results show the effectiveness and efficiency of the proposed method.