A tensor-based approach to Dempster-Shafer computing for evidence combination

Dempster’s rule of combination serves as a cornerstone in evidence theory for integrating uncertain information from multiple sources. Despite its theoretical appeal, practical implementations remain computationally demanding, especially when relying on recursive algorithms or the Fast Möbius Transform. In this paper, we introduce a novel tensor-based framework for efficient evidence combination under Dempster’s rule. By reformulating mass function aggregation as a tensor operation, our approach enables seamless integration with modern machine learning and data processing pipelines that natively support tensor computations. The core mechanism involves applying a projection matrix to the dyadic product of two encoded mass functions, precisely emulating traditional combination behavior. Extensive empirical evaluations demonstrate significant computational advantages, including up to a 1000-fold acceleration in worst-case scenarios compared to classical recursive methods. This work not only provides a computationally efficient and tensor-compatible alternative for real-time information fusion tasks but also lays the foundation for future research at the intersection of evidential reasoning and tensor-based computation. To support reproducibility and facilitate further research, we also provide our implementation of the developed tensor-based methodology as open-source software which is publicly accessible. 1 1 https://pypi.org/project/TensorBeliefFunction/ .