Elimination and Combination of Classifiers in Multiple Classifier Systems

Traditional character recognition systems use a single classifier to determine the class of a given character. However, by using different types of classifiers simultaneously, the accuracy of classification could be improved. In this paper, we propose a classifier elimination approach based on correlation analysis and the derived heuristic rules to eliminate the redundant classifiers such that the succeeding decision combination process can be conducted in a more efficient and effective manner. Experimental results show our approach works well in the multiple classifier system.