Diversity is an important consideration in classifier ensembles, it can be potentially expolited in order to obtain a higher classification accuracy. There is no widely accepted formal definition of diversity in classifier ensembles, thus making an objective evaluation of diversity measures difficult. We propose a set of properties and a linear program based framework for the analysis of diversity measures for ensembles of binary classifiers. Although we regard the question of what exactly defines diversity in a classifier ensemble as open, we show that the framework can be used effectively to evaluate diversity measures. We explore whether there is a useful relationship between the selected diversity measures and the ensemble accuracy. Our results cast doubt on the usefulness of diversity measures in designing a classifier ensemble, although the motivation for enforcing diversity in a classifier ensemble is justified. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Narasimhamurthy, A. (2005). Evaluation of diversity measures for binary classifier ensembles. In Lecture Notes in Computer Science (Vol. 3541, pp. 267–277). Springer Verlag. https://doi.org/10.1007/11494683_27
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