An Adaptive Weighted Majority Vote Rule for Combining Multiple Classifiers

We introduce a novel multiple classifier system that incorporates a global optimization technique based on a genetic algorithm for configuring the system. The system adopts the weighted majority vote approach to combine the decision of the experts, and obtains the weights by maximizing the performance of the whole set of experts, rather than that of each of them separately. The system has been tested on a handwritten digit recognition problem, and its performance compared with those exhibited by a system using the weights obtained during the training of each expert separately. The results of a set of experiments conducted on 30,000 digits extracted from the NIST database have shown that the proposed system exhibits better performance than those of the alternative one, and that such an improvement is due to a better estimate of the reliability of the participating classifiers.