Analyzing the degree of conflict among belief functions

By Weiru Liu. Artificial Intelligence, 2006

435 citations recorded by the source corpus.

The study of alternative combination rules in DS theory when evidence is in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. These studies have mainly focused on investigating which alternative would be appropriate for which conflicting situation, under the assumption that a conflict is identified. The issue of detection (or identification) of conflict among evidence has been ignored. In this paper, we formally define when two basic belief assignments are in conflict. This definition deploys quantitative measures of both the mass of the combined belief assigned to the emptyset before normalization and the distance between betting commitments of beliefs. We argue that only when both measures are high, it is safe to say the evidence is in conflict. This definition can be served as a prerequisite for selecting appropriate combination rules.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/94e382d7be2841c0354b325196b745b68a9f5f8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Philippe Smets
  2. Weiru Liu
  3. Jianbo Yang
  4. Dongling Xu
  5. Christoph Schmidt-Petri
  6. Franz Huber
  7. Florentin Smarandache
  8. Jean Dezert

In Computational Theory and Mathematics

  1. Philippe Smets
  2. Florentin Smarandache
  3. Jean Dezert

In Management Science and Operations Research

  1. Philippe Smets
  2. Weiru Liu
  3. Dongling Xu
  4. Jianbo Yang
  5. Christoph Schmidt-Petri
  6. Franz Huber

Citations in the corpus