The success of information fusion is defined by the quality of knowledge produced by fusion
processes, with the latter in turn depending on how well data are represented, how reliable …

Within the framework of evidence theory, data fusion consists in obtaining a single belief
function by the combination of several belief functions resulting from distinct information …

We consider uncertain data which uncertainty is represented by belief functions and that
must be combined. The result of the combination of the belief functions can be partially …

Single-modality medical images generally do not contain enough information to reach an
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …

Due to its major focus on knowledge representation and reasoning, artificial intelligence was
bound to deal with various frameworks for the handling of uncertainty: probability theory, but …

In the previous issue of this journal, Lefevre et al.'s paper [1] raises the important question of
how to combine belief functions from different sources and how to manage conflicts. They …

In this paper, conventional D–S evidence theory is improved through the introduction of
fuzzy membership function, importance index, and conflict factor in order to address the …

Dempster–Shafer evidence theory is widely used in many applications such as decision
making and pattern recognition. However, Dempster's combination rule often produces …

In belief functions theory, the discounting operation allows to combine information provided
by a source in the form of a belief function with meta-knowledge regarding the reliability of …

This paper presents a new framework for sensor reliability evaluation in classification
problems based on evidence theory (or the Dempster–Shafer theory of belief functions). The …