Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition

A recent trend in the field of pattern recognition has been the use of ensemble classifiers. If combined properly, the ensemble can achieve a higher identification rate than any individual classifier. Plurality voting is one of the most commonly used combination strategies. The performance of plurality voting can be improved if the decisions of different classifiers are weighted properly. In this paper, we both theoretically and experimentally analyze the performance of a weighted plurality voting combination strategy to combine the decisions of multiple classifiers. Theoretical expressions characterizing the performance of the weighted voting model are derived and the method is applied to the problem of human face recognition and voice recognition. The results show the advantage of employing weighted-voting-based ensemble classifiers in achieving high identification rates.