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Combining the Results of Several Neural Network Classifiers

  • Galina Rogova
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 219)

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.

Keywords

Classifier Neural network Character recognition The Dempster–Shafer theory of evidence Evidence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Galina Rogova

There are no affiliations available

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