DPT: An importance-based decision probability transformation method for uncertain belief in evidence theory

Abstract

Dempster–Shafer evidence theory can combine multiple sources of uncertain information to form a consistent basic probability assignment (BPA). How to transform BPA into decision probability is still an open issue. To solve this problem, an importance-based Decision Probability Transformation (DPT) method is proposed in this paper. In this method, the importance weight is introduced into the belief interval for the first time, and then the importance is used to define the information volume and superiority degree, so that more reliable decisions can be achieved through the trade-off between conservative and aggressive. Comparative experiments on numerical examples and applications show that the proposed DPT method can weigh the aggressiveness of decisions, and its decision probability transformation behavior is the most rational and its application performance is the most reliable. Especially, the accuracy rate of classification application is 95.31%, which is at least 0.54% higher than other compared methods.