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Entropy
Volume 22
Issue 8
10.3390/e22080801
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Article

Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory

by
Shuang Ni
,
Yan Lei
and
Yongchuan Tang
*
School of Big Data and Software Engineering, Chongqing University, Chongqing 401331, China
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(8), 801; https://doi.org/10.3390/e22080801
Submission received: 19 June 2020 / Revised: 14 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
(This article belongs to the Section Signal and Data Analysis)
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Abstract

Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed. In this paper, the belief entropy in D–S theory is used to measure the uncertainty in each evidence. First, the initial belief degree is constructed by using an improved base belief function. Then, the information volume of each evidence group is obtained through calculating the belief entropy which can modify the belief degree to get the final evidence that is more reasonable. Using the Dempster combination rule can get the final result after evidence modification, which is helpful to solve the conflict data fusion problems. The rationality and validity of the proposed method are verified by numerical examples and applications of the proposed method in a classification data set.
Keywords: Dempster-Shafer theory; coflict data fusion; improved base belief function; information volume; belief entropy Dempster-Shafer theory; coflict data fusion; improved base belief function; information volume; belief entropy

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MDPI and ACS Style

Ni, S.; Lei, Y.; Tang, Y. Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory. Entropy 2020, 22, 801. https://doi.org/10.3390/e22080801

AMA Style

Ni S, Lei Y, Tang Y. Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory. Entropy. 2020; 22(8):801. https://doi.org/10.3390/e22080801

Chicago/Turabian Style

Ni, Shuang, Yan Lei, and Yongchuan Tang. 2020. "Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory" Entropy 22, no. 8: 801. https://doi.org/10.3390/e22080801

APA Style

Ni, S., Lei, Y., & Tang, Y. (2020). Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory. Entropy, 22(8), 801. https://doi.org/10.3390/e22080801

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