Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

An Efficient Method to Measure Evidence Conflict

Authors
Baojie Liu, Qingwen Yang, Xiang Wu, Yujuan Guo, Shidong Fang
Corresponding Author
Baojie Liu
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.109How to use a DOI?
Keywords
D-S evidence theory; evidence conflict; jaccard similarity coefficient ; conflict measure; information fusion
Abstract
Dempster Shafer evidence theory, as an uncertain information fusion technology, is widely used in various fields of information fusion. However, when there is a highly conflict between two pieces of evidence, counterintuitive results are obtained by classical Dempster's combination rule. Therefore, it is very important to measure the conflict between two pieces of evidence. Based on the analysis of some typical conflict measurement methods, a new model to represent conflict was constructed with the Euclidean distance function and the Jaccard similarity coefficient. Some numerical examples illustrate that the proposed method can measure the degree of conflict between the two pieces of evidence and overcome the shortcomings of the classical conflictive coefficient, Jousselme evidence distance and Pignistic probability distance to a certain extent.
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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.109How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Baojie Liu
AU  - Qingwen Yang
AU  - Xiang Wu
AU  - Yujuan Guo
AU  - Shidong Fang
PY  - 2017/06
DA  - 2017/06
TI  - An Efficient Method to Measure Evidence Conflict
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 483
EP  - 488
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.109
DO  - https://doi.org/10.2991/caai-17.2017.109
ID  - Liu2017/06
ER  -