A New Similarity Measure for Combining Conflicting Evidences

Abstract

In Dempster-Shafer (DS) theory, multiple information from the distinct information sources are combined to obtain a single Basic Probability Assignment (BPA) function. The well-known combination rule of Dempster-Shafer (DS) provides the weaker solution to the management of conflicting information at the normalization stage. Even this rule fails and provide the counter intuitive results while combining the highly conflicting information. This paper presents a new similarity measure for the combined average methods, where any distance measure between the body of information can be used. The numerical examples provide the promising and better intuitive results.