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

TL;DR

This paper introduces FNBT, a novel open-world information fusion method based on Dempster-Shafer theory, capable of handling heterogeneous data sources and improving pattern classification accuracy in uncertain environments.

Contribution

The paper proposes a full negation belief transformation approach that extends frames and transforms mass functions, addressing open-world fusion challenges with formal properties and empirical validation.

Findings

FNBT outperforms traditional methods in pattern classification tasks.

Successfully resolves Zadeh's counterexample.

Theoretically proven properties include invariance, heritability, and conflict elimination.

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.