The impact of diversity on the accuracy of evidential classifier ensembles

Diversity being inherent in classifiers is widely acknowledged as an important issue in constructing successful classifier ensembles. Although many statistics have been employed in measuring diversity among classifiers to ascertain whether it correlates with ensemble performance in the literature, most of these measures are incorporated and explained in a non-evidential context. In this paper, we provide a modeling for formulating classifier outputs as triplet mass functions and a uniform notation for defining diversity measures. We then assess the relationship between diversity obtained by four pairwise and non-pairwise diversity measures and the improvement in accuracy of classifiers combined in different orders by Demspter's rule of combination, Smets' conjunctive rule, the Proportion and Yager's rules in the framework of belief functions. Our experimental results demonstrate that the accuracy of classifiers combined by Dempster's rule is not strongly correlated with the diversity obtained by the four measures, and the correlation between the diversity and the ensemble accuracy made by Proportion and Yager's rules is negative, which is not in favor of the claim that increasing diversity could lead to reduction of generalization error of classifier ensembles. (C) 2012 Elsevier Inc. All rights reserved.