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 …

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 …

In this article we develop a method for conflict management within Dempster–Shafer theory.
The idea is that each piece of evidence is discounted in proportion to the degree that it …

Median clustering is of great value for partitioning relational data. In this paper, a new
prototype-based clustering method, called Median Evidential C-Means (MECM), which is an …

Recursive processing of Gaussian mixture functions inevitably leads to a large number of
mixture components. In order to keep the computational complexity at a feasible level, the …

Counter-deception information fusion is a significant issue in Dempster–Shafer evidence
theory (DST). How to effectively counter the deception is the key problem in belief function …

We develop a method for clustering all types of belief functions, in particular non-consonant
belief functions. Such clustering is done when the belief functions concern multiple events …

In this work, a new prototype-based clustering method named Evidential C-Medoids
(ECMdd), which belongs to the family of medoid-based clustering for proximity data, is …

The Swedish Defence Research Agency (FOI) has developed a concept demonstrator
called the Information Fusion Demonstrator 2003 (IFD03) for demonstrating information …

In this paper we define and derive an internal conflict of a belief function We decompose the
belief function in question into a set of generalized simple support functions (GSSFs) …