BPEC: Belief-Peaks Evidential Clustering

Evidential Clustering: A Review

In evidential clustering, uncertainty about the assignment of objects to clusters is represented by Dempster-Shafer mass functions. The resulting clustering structure, called a credal partition, is shown to be more general than hard, fuzzy, possibilistic and rough partitions, which are recovered as special cases. Three algorithms to generate a credal partition are reviewed. Each of these algori...

Evidential Reasoning with Conditional Belief Functions

In the existing evidential networks with belief functions, the relations among the variables are always represented by joint belief functions on the product space of the involved variables. In this paper, we use conditional belief functions to represent such relations in the network and show some relations of these two kinds of representations. We also present a propagation algorithm for such n...

Belief Function Propagation in Directed Evidential Networks

In this paper, we propose a computational data structure based on the binary join tree where the independence relations of the original directed evidential networks (DEVN) are maintained. The proposed solution uses disjunctive rule of combination (DRC) and generalized Bayesian theorem (GBT), which make possible the use of the conditional belief functions directly for reasoning in the DEVN.

Continuous Belief Functions for Evidential Reasoning

Some recently developed expert systems have used the ShaferDempster theory for reasoning from multiple bodies of evidence. Many expert-system applications require belief to be specified over arbitrary ranges of scalar variables, such as time, distance or sensor measurements. The utility of the existing ShaferDempster theory is limited by the lack of an effective approach for dealing with belief...

Truth Promoting Non-Evidential Reasons for Belief