Comparing diversity and training accuracy in classifier selection for plurality voting based fusion

Abstract

Selection of an optimal subset of classifiers in designing classifier ensembles is an important problem. The search algorithms used for this purpose maximize an objective function which may be the combined training accuracy or diversity of the selected classifiers. Taking into account the fact that there is no benefit in using multiple copies of the same classifier, it is generally argued that the classifiers should be diverse and several measures of diversity are proposed for this purpose. In this paper, the relative strengths of combined training accuracy and diversity based approaches are investigated for the plurality voting based combination rule. Moreover, we propose a diversity measure where the difference in classification behavior exploited by the plurality voting combination rule is taken into account.