An optimal probabilistic transformation of belief functions based on artificial bee colony algorithm

Abstract

One of the key issues in the applications of Dempster-Shafer evidence theory is how to make a decision based on Basic Probability Assignment (BPA). Besides the widely known pignistic transformation of belief functions, another conventional method is Plausibility Transformation, which transforms BPA to probability distribution by normalizing plausibility function of every singleton proposition. However, these two methods are stuck with the problem of impreciseness. To overcome this drawback, a new transformation method based on Artificial Bee Colony Algorithm is proposed. The obtained probability has the maximum correlation coefficient with the original BPA. Numerical examples are used to illustrate the efficiency of the proposed method. © 2014 Springer International Publishing Switzerland.