The impact of diversity on the accuracy of evidential classifier ensembles

Diversity being inherent in classifiers is widely acknowledged as an important issue in constructingsuccessful classifier ensembles. Although many statistics have been employed inmeasuring diversity among classifiers to ascertainwhether it correlates with ensemble performancein the literature, most of these measures are incorporated and explained in anon-evidential context. In this paper, we provide a modeling for formulating classifier outputsas triplet mass functions and a uniform notation for defining diversity measures. Wethen assess the relationship between diversity obtained by four pairwise and non-pairwisediversitymeasures and the improvement in accuracy of classifiers combined in different ordersby Demspter’s rule of combination, Smets’ conjunctive rule, the Proportion and Yager’srules in the framework of belief functions. Our experimental results demonstrate that theaccuracy of classifiers combined by Dempster’s rule is not strongly correlated with thediversity obtained by the four measures, and the correlation between the diversity andthe ensemble accuracy made by Proportion and Yager’s rules is negative, which is not infavor of the claim that increasing diversity could lead to reduction of generalization error ofclassifier ensembles.