Fractal belief Jensen–Shannon divergence-based multi-source information fusion for pattern classification

Abstract

Multi-source information fusion is an effective method to handle pattern classification problems. Dempster–Shafer evidence theory (DSET) plays an important role in handling uncertainty problems in multi-source information fusion. However, highly conflicting evidence in DSET may cause counter-intuitive fusion results. Belief divergence theory is one of the solutions to conflict management, which is also beneficial for the improvement of accuracies of pattern classification. In this paper, a novel belief divergence measurement method, fractal belief Jensen–Shannon (FBJS) divergence is proposed to better measure the discrepancy between Basic probability assignments (BPAs) and address the problem of highly conflicting evidence in DSET. The proposed FBJS divergence is the first belief divergence that incorporates the belief divergence theory and the concept of fractal. In addition, it has the properties of non-negativeness and symmetry. Then, based on FBJS divergence, a novel multi-source information fusion algorithm is proposed. Ultimately, the proposed algorithm is effectively applied to solve a pattern classification problem with a higher classification accuracy.