Dempster–Shafer evidence theory (DSET) is extensively employed in multi-source data
fusion applications. Nonetheless, when belief probability assignments (BPAs) exhibit …

Abstract Dempster–Shafer (D–S) evidence theory is useful in the realm of multi-source data
fusion. However, a counterintuitive result may be obtained when the belief probability …

Multi-source information fusion is an effective method to handle pattern classification
problems. Dempster–Shafer evidence theory (DSET) plays an important role in handling …

How to manage conflict in Dempster–Shafer (DS) evidence theory is still an open problem.
To address this problem, a novel divergence measure is proposed to measure the distance …

Multi-source information fusion is a sophisticated estimation process that generates a unified
profile to assess complex situations. Dempster–Shafer evidence theory (DSET) is a practical …

Multi-source information fusion technology has been widely used because it can maximize
the use of information that collected from multiple data sources for decision fusion. As an …

Dempster–Shafer evidence theory is an effective method to deal with information fusion.
However, how to deal with the fusion paradoxes while using the Dempster's combination …

Dempster-Shafer evidence theory (DST) is a versatile framework for handling uncertainty
and provides a reliable method for data fusion. Managing conflicts between multiple bodies …

Belief functions theory (BFT) plays a critical role in addressing uncertainty and imprecision in
multi-source data fusion. Unfortunately, the application of Dempster's rule in BFT often …

The application of multisource information fusion in real-world scenarios is an emerging
practice because it effectively uses consistent and complementary data to optimize decision …