An Optimal Method for Combining Conflicting Evidences

Abstract

Most researchers hold that revising mass function based methods are reasonable to deal with the problem of conflicting evidence combination. However, the existing methods of revising mass function only consider improving focusing degree of combination results. Actually, they did not effectively reduce conflict among evidences by revision. Obviously, the fusion result of conflicting evidences has low credibility and will certainly bring risks to subsequent fusion process. To solve this problem, by adopting the idea of discounting, this paper proposes an optimal model to learn discounting factors (reliability) based on evidence distance criterion which considers improving focusing degree and reducing conflict simultaneously. The procedures of optimization are achieved through minimizing the distance between combined basic probability assignment (BPA) of revised mass function and categorical BPA (CBPA). The permutation of reliabilities associated with evidences, which is regarded as constraint condition, is determined according to their falsity. Typical examples illustrate that the presented method is more reasonable than some existing methods both in reducing conflict and improving focusing degree.