FNBT: Full Negation Belief Transformation for Open-World Information Fusion Based on Dempster-Shafer Theory of Evidence

2025-08-11

AI Summary

In open-world heterogeneous recognition frameworks, information fusion is hindered by the fact that multi-source data—due to geographical or organizational isolation—are associated with basic probability assignments defined on distinct frames of discernment, rendering classical Dempster–Shafer (D-S) evidence theory inapplicable. Method: This paper proposes a cross-frame fusion method based on total-negation belief transformation. It constructs a unified frame via frame expansion and mass function mapping, and incorporates an open-world decision criterion alongside a total-negation mechanism to resolve intrinsic conflicts while preserving mass function invariance and inheritability. Contribution/Results: Integrated with the classical D-S combination rule, the proposed method significantly improves classification accuracy on real-world datasets and rigorously resolves Zadeh’s counterexample, thereby demonstrating both theoretical soundness and practical efficacy.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

The Dempster-Shafer theory of evidence has been widely applied in the field of information fusion under uncertainty. Most existing research focuses on combining evidence within the same frame of discernment. However, in real-world scenarios, trained algorithms or data often originate from different regions or organizations, where data silos are prevalent. As a result, using different data sources or models to generate basic probability assignments may lead to heterogeneous frames, for which traditional fusion methods often yield unsatisfactory results. To address this challenge, this study proposes an open-world information fusion method, termed Full Negation Belief Transformation (FNBT), based on the Dempster-Shafer theory. More specially, a criterion is introduced to determine whether a given fusion task belongs to the open-world setting. Then, by extending the frames, the method can accommodate elements from heterogeneous frames. Finally, a full negation mechanism is employed to transform the mass functions, so that existing combination rules can be applied to the transformed mass functions for such information fusion. Theoretically, the proposed method satisfies three desirable properties, which are formally proven: mass function invariance, heritability, and essential conflict elimination. Empirically, FNBT demonstrates superior performance in pattern classification tasks on real-world datasets and successfully resolves Zadeh's counterexample, thereby validating its practical effectiveness.

Problem

• Addresses fusion of heterogeneous evidence from different data sources
• Proposes FNBT for open-world information fusion under uncertainty
• Ensures compatibility with existing combination rules via transformation

Innovation

• FNBT method for open-world information fusion
• Extends frames to handle heterogeneous data
• Uses full negation to transform mass functions