A Weighted Majority Vote Strategy Using Bayesian Networks

Most of the methods for combining classifiers rely on the assumption that the experts to be combined make uncorrelated errors. Unfortunately, this theoretical assumption is not easy to satisfy in practical cases, thus effecting the performance obtainable by applying any combination strategy. We tried to solve this problem by explicitly modeling the dependencies among the experts through the estimation of the joint probability distributions among the outputs of the classifiers and the true class. In this paper we propose a new weighted majority vote rule, that uses the joint probabilities of each class as weights for combining classifier outputs. A Bayesian Network automatically infers the joint probability distribution for each class. The final decision is made by taking into account both the votes received by each class and the statistical behavior of the classifiers. The experimental results confirmed the effectiveness of the proposed method.