A novel divergence measure of mass function for conflict management

A Novel Method for Conflict Data Fusion Using an Improved Belief Divergence Measure in Dempster–Shafer Evidence Theory

Mathematical Problems in Engineering ◽

10.1155/2021/6558843 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yifan Liu ◽

Tiantian Bao ◽

Huiyun Sang ◽

Zhaokun Wei

Keyword(s):

Data Fusion ◽

Conflict Management ◽

Evidence Theory ◽

External Support ◽

Divergence Measure ◽

Fusion Method ◽

Combination Rule ◽

Special Cases ◽

Novel Method ◽

Belief Entropy

Dempster–Shafer (D-S) evidence theory plays an important role in multisource data fusion. Due to the nature of the Dempster combination rule, there can be counterintuitive results when fusing highly conflicting evidence data. To date, conflict management in D-S evidence theory is still an open issue. Inspired by evidence modification considering internal indeterminacy and external support, a novel method for conflict data fusion is proposed based on an improved belief divergence, evidence distance, and belief entropy. First, an improved belief divergence measure is defined to characterize the discrepancy and conflict between bodies of evidence (BOEs). Second, evidence credibility is generated to describe the external support based on the complementary advantages of the improved belief divergence and evidence distance. Third, belief entropy is utilized to quantify the internal indeterminacy and further determine evidence weight. Lastly, the classical Dempster combination rule is applied to fuse the BOEs modified by their credibility degrees and weights. As the results of numerical examples and an application show, the proposed divergence measure can overcome the invalidity of the existing measures in some special cases. Additionally, the proposed fusion method recognizes the correct target with the highest belief value of 98.96%, which outperforms other related methods in conflict management. The proposed fusion method also displays better convergence, validity, and robustness.

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CBD: A New Divergence Measure for Complex Mass Function and its Application in Pattern Recognition

10.21203/rs.3.rs-579531/v1 ◽

2021 ◽

Author(s):

Lan Luo ◽

Fuyuan Xiao

Keyword(s):

Pattern Recognition ◽

Function Theory ◽

Evidence Theory ◽

Mass Function ◽

Recognition Algorithm ◽

Divergence Measure ◽

Pattern Recognition Algorithm ◽

Complex Mass ◽

Open Issue ◽

Uncertainty Information

Abstract The theory of complex mass function is an effective method to deal with uncertainty information, and it is a generalized of Dempster-Shafer evidence theory. However, divergence measure is still an open issue in the realm of complex mass function theory. The main contribution of our paper is to propose a generalized divergence measure for complex mass function that is called complex belief divergence (CBD),which has the properties of symmetry, nonnegativity, nondegeneracy. When complex mass function degenerates into classical mass function, the CBD will degenerate into classical belief divergence, which has a better ability to measure uncertainty of information. Finally, a pattern recognition algorithm based on CBD is designed and applied to a medical diagnosis problem, which proves its practical prospect.

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