Multiscale probability transformation of basic probability assignment
This work addresses decision-making challenges in evidence theory for applications like uncertainty modeling, but it appears incremental as it builds on existing pignistic probability methods.
The paper tackles the problem of decision making in Dempster-Shafer evidence theory by proposing a multiscale probability transformation of basic probability assignment, which generalizes the pignistic probability transformation and uses a Tsallis entropy-based factor to diversify probabilities, showing it is more reasonable in decision making through an example.
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.