Combining the results of several neural network classifiers

Abstract

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. The method allows 15-30%reduction of misclassification error compared to the best individual classifier. © 2008 Springer-Verlag Berlin Heidelberg.