An evidence combination approach based on fuzzy discounting

Abstract

AbstractIn evidence theory, Dempster’s rule of combination is the most commonly applied method to aggregate bodies of evidence obtained from different sources to make a decision. However, when multiple independent bodies of evidence with conflict are aggregated by Dempster’s rule of combination, the counterintuitive results can be generated. Evidence discounting is proved to be an efficient way to eliminate the counterintuitive combination results. Following the discounting ideas, a new combination approach based on fuzzy discounting is put forward. Both the conflict between bodies of evidence and the uncertainty of a body of evidence itself are taken into account to determine the discounting factors. Jousselme’s evidence distance is used to represent conflict between bodies of evidence, and discriminability measure is defined to represent uncertainty of a body of evidence itself. Consider that both the evidence distance and the discriminability measure are semantically fuzzy. Thus, fuzzy membership functions are defined to describe both of them, and a fuzzy reasoning rule base is constructed to derive the discounting factors. Numerical examples indicate that this new combination approach proposed can achieve fast convergence speed and is robust to disturbing evidences, i.e., it is an effective method to process conflicting evidences combination.