Combining the results of several neural network classifiers

Neural networks and traditional classifiers work well for optical character recognition; however, it is advantageous to combine the results of several algorithms to improve classification accuracies. This paper presents a combination method based on the Dempster-Shafer theory of evidence, which uses statistical information about the relative classification strengths of several classifiers. Numerous experiments show the effectiveness of this approach. Our method allows 15-30% reduction of misclassification error compared to the best individual classifier.