Multiscale probability transformation of basic probability assignment

TL;DR

This paper introduces a multiscale probability transformation method based on belief and plausibility functions in Dempster-Shafer theory, using Tsallis entropy to improve decision-making processes.

Contribution

It generalizes the pignistic probability transformation by incorporating a multiscale factor derived from Tsallis entropy, enhancing decision-making in evidence theory.

Findings

Multiscale probability transformation is more reasonable for decision making.

The method diversifies probabilities using a Tsallis entropy-based factor.

Example demonstrates improved decision-making performance.

Abstract

Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability as- signments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factor q based on the Tsallis entropy is used to make the multiscale prob- abilities diversified. An example is shown that the multiscale probability transformation is more reasonable in the decision making.