Transformation of basic probability assignments to probabilities based on a new entropy measure

TL;DR

This paper introduces a new method to convert basic probability assignments into probabilities using Deng entropy, aiming to improve decision-making in Dempster-Shafer evidence theory.

Contribution

It proposes a novel transformation approach based on Deng entropy, addressing the open issue of decision-making from BPA in evidence theory.

Findings

The method effectively minimizes uncertainty differences.

Numerical examples demonstrate the approach's validity.

Improves decision-making accuracy in evidence theory.

Abstract

Dempster-Shafer evidence theory is an efficient mathematical tool to deal with uncertain information. In that theory, basic probability assignment (BPA) is the basic element for the expression and inference of uncertainty. Decision-making based on BPA is still an open issue in Dempster-Shafer evidence theory. In this paper, a novel approach of transforming basic probability assignments to probabilities is proposed based on Deng entropy which is a new measure for the uncertainty of BPA. The principle of the proposed method is to minimize the difference of uncertainties involving in the given BPA and obtained probability distribution. Numerical examples are given to show the proposed approach.