An improved soft likelihood function for Dempster-Shafer belief structures

Abstract

Information fusion is an important research direction. In the field of information fusion, there are many methods for evidence combination. Recently, Yager proposed a method of soft likelihood function to combine probabilistic evidence effectively. Considering that basic probability assignment (BPA) can deal with uncertainty information more effectively, in this paper, we extend Yager's soft likelihood function to combine BPA. First, according to the BPA evaluations of evidence sources, belief function and plausibility function on each alternative are calculated. Then, interval numbers are constructed by the obtained belief function and plausibility function to indicate the belief interval on each alternative. Next, the descending sorting of interval numbers is aggregated by the ordered weighted averaging operator. Finally, by sorting the result of the aggregation, the ordering of alternatives is obtained. A numerical example and an example of application in Iris data set classification illustrate the effectiveness of the improved method.