Analysis of a plurality voting-based combination of classifiers

In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), m (number of classes), and p (probability that a single classifier is correct). Experimental results on the problem of human face recognition show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates.