A Cluster-Level Information Fusion Framework for D-S Evidence Theory with Its Applications in Pattern Classification

Minghao Ma and Liguo Fei ()
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Minghao Ma: Department of Computer Science, University of Liverpool, Liverpool L69 7ZX, UK
Liguo Fei: Smart State Governance Lab, Shandong University, Qingdao 266237, China

Mathematics, 2025, vol. 13, issue 19, 1-46

Abstract: Multi-source information fusion is a key challenge in uncertainty reasoning. Dempster–Shafer evidence theory (D-S evidence theory) offers a flexible framework for representing and fusing uncertain information. However, the classical Dempster’s combination rules may yield counter-intuitive results when faced with highly conflicting evidence. To overcome this limitation, we introduce a cluster-level information fusion framework, which shifts the focus from pairwise evidence comparisons to a more holistic cluster-based perspective. A key contribution is a novel cluster–cluster divergence measure that jointly captures the strength of belief assignments and structural differences between clusters. Guided by this measure, a reward-driven evidence assignment rule dynamically allocates new evidence to enhance inter-cluster separability while preserving intra-cluster coherence. Building upon the resulting structure, we propose a two-stage information fusion algorithm that assigns credibility weights at the cluster level. The effectiveness of the framework is validated through a range of benchmark pattern classification tasks, in which the proposed method not only improves classification accuracy compared with D-S evidence theory methods but also provides a more interpretable, cluster-oriented perspective for handling evidential conflict.

Keywords: D-S evidence theory; basic belief assignment; cluster; divergence; multi-source information fusion; pattern classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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