# Evidential Information Fusion on Possibilistic Structure

arXiv: https://arxiv.org/abs/2605.17038 · HTML: https://mlsanity.com/paper/2605.17038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 16 May 2026.

## Builds on (most cited first, 4 of 4)

- [Expert Opinion Extraction from a Biomedical Database](https://mlsanity.com/paper/1709.03270/) (2017)
- [Belief Evolution Network-based Probability Transformation and Fusion](https://mlsanity.com/paper/2110.03468/) (2021)
- [Isopignistic Canonical Decomposition via Belief Evolution Network](https://mlsanity.com/paper/2405.02653/) (2024)
- [Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection](https://mlsanity.com/paper/2505.06516/) (2025)

## Abstract (arXiv)

Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in handling complex source states and diverse information fusion scenarios. To overcome this limitation, we propose a reversible transformation, derived from the isopignistic principle, between belief functions and a possibilistic structure defined on the power set. In this transformation, the relationships among subsets are explicitly characterized by a belief evolution network, which provides a more flexible representation of evidential information beyond the conventional mass function structure. On this basis, we further introduce the triangular norm family to develop a general and adaptive evidential information fusion framework. Unlike fusion methods rooted in Dempster semantics, the proposed framework supports more flexible combination behaviors and exhibits advantages in non-distinct source fusion, conflict management, parametric combination design, and heterogeneous information fusion.