Data fusion for pattern classification via the Dempster-Shafer evidence theory

This paper presents a novel technique to fuse multi information sources for the purpose of pattern classification. The proposed data fusion technique is based on the Dempster-Shafer evidence theory. Mass functions are derived from probabilistic and fuzzy measures that are associated with discriminant functions for pattern classification. Simulated synthetic images as well as real human brain magnetic resonance images (MRI) are tested to demonstrate the performance and effectiveness of the proposed approach. It is concluded from the experimental results that the proposed algorithm is quite effective and superior to other approaches such as the Bayesian approach. Furthermore, the paper explains how this approach exhibits a capability to handle uncertainty, imprecision and conflicts which often hinders multi information fusion.