Belief Evolution Network-based Probability Transformation and Fusion

TL;DR

This paper introduces the Belief Evolution Network and the Full Causality Probability Transformation, offering improved probability fusion methods within the Transferable Belief Model for better decision-making under uncertainty.

Contribution

It proposes the Belief Evolution Network and the Full Causality Probability Transformation, advancing probability transformation and fusion techniques in belief models.

Findings

FCPT outperforms existing methods under Bi-Criteria evaluation.

The new fusion method yields more reasonable results with similar evidence.

BEN provides a novel interpretation of PPT from an information fusion perspective.

Abstract

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.