A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence

Details A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence

2011-06-30

arxiv.org/abs/1107.0018v1

Journal Of Artificial Intelligence Research, Volume 17, pages 333-361, 2002

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.1026

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.

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