A Dempster-Shafer theory of evidence approach for combining trained neural networks
The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since there is not a unique way to perform such a combination, we have developed an algorithm which adapts to the training data set so that the overall mean square error is minimised. The proposed method was proved to be superior and more robust than other available combination methods.