Ten measures of diversity in classifier ensembles: limits for two classifiers

Independence and dependence of classifier outputs have been debated in the recent literature giving rise to notions such as diversity, complementarity, orthogonality, etc. There seems to be no consensus on the meaning of these notions beyond the intuitive perception. We summarize 10 measures of classifier diversity: 4 pairwise and 6 non-pairwise measures. We derive the limits of the measures for 2 classifiers of equal accuracy.