An asymptotic analysis of some expert fusion methods

We study the asymptotic behavior of three classifier combination methods for two-class classification: Average, median, and majority vote. Assuming that the estimates of the posterior probability given by individual classifiers constitute a sample from a distribution, we show that as the number of individual classifiers becomes large, median and majority will produce the same result but average may yield a completely different decision if the distribution is not symmetric.