A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance analysis of pattern classifier combination by plurality voting
Pattern Recognition Letters
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Weighted Voting Model of Associative Memory
IEEE Transactions on Neural Networks
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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.