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