A New Technique for Combining Multiple Classifiers using The
  Dempster-Shafer Theory of Evidence
  This paper presents a new classifier combination technique based on the
Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a
powerful method for combining measures of evidence from different classifiers.
However, since each of the available methods that estimates the evidence of
classifiers has its own limitations, we propose here a new implementation which
adapts to training data so that the overall mean square error is minimized. The
proposed technique is shown to outperform most available classifier combination
methods when tested on three different classification problems.
