That Elusive Diversity in Classifier Ensembles

Abstract

AbstractIs “useful diversity” a myth? Many experiments and the little available theory on diversity in classifier ensembles are either inconclusive, too heavily assumption-bound or openly non-supportive of the intuition that diverse classifiers fare better than non-divers ones. Although a rough general tendency was confirmed in our previous studies, no prominent link appeared between diversity of the ensemble and its accuracy. Diversity alone is a poor predictor of the ensemble accuracy. But there is no agreed definition of diversity to start with! Can we borrow a concept of diversity from biology? How can diversity, as far as we can define and measure it, be used to improve the ensemble? Here we argue that even without a clear-cut definition and theory behind it, studying diversity may prompt viable heuristic solutions. We look into some ways in which diversity can be used in analyzing, selecting or training the ensemble.KeywordsConvex HullRandom ForestClass LabelEnsemble MemberCombination MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.