Belief Evolution Network-based Probability Transformation and Fusion
This work addresses incremental improvements in decision-making and information fusion for belief function theory, relevant to researchers in uncertainty reasoning.
The paper tackles the problem of probability transformation and fusion in belief functions by proposing a Full Causality Probability Transformation (FCPT) method based on a Belief Evolution Network, which shows better performance under Bi-Criteria evaluation and yields more reasonable results than the Dempster Rule of Combination when fusing evidence.
Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence.