A D-S based multi-channel information fusion method using classifier's uncertainty measurement

Classifiers of different types complement one another in classification performance, and there exists uncertainty in the outputs of classifiers, which reflects the reliability of the classification results. However, this uncertainty is not considered in traditional fusion method. In this paper, geometrically inspired measurement of uncertainty is proposed which is integrated when combining multiple classifiers using D-S evidence theory. Numerical analysis results show that this method is effective and reasonable.