BGC: Belief gravitational clustering approach and its application in the counter-deception of belief functions

被引:7
作者
Cui, Huizi [1 ]
Zhang, Huaqing [1 ]
Chang, Yuhang [1 ]
Kang, Bingyi [1 ,2 ,3 ]
机构
[1] Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Shaanxi, Peoples R China
[2] Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China
[3] Shaanxi Key Lab Agr Informat Percept & Intelligent, Yangling 712100, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Dempster-Shafer evidence theory (DST); Belief gravitation; Counter-deception; Information fusion; Conflict management; Clustering; DIVERGENCE MEASURE; COMBINATION; FRAMEWORK; NUMBER;
D O I
10.1016/j.engappai.2023.106235
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Counter-deception information fusion is a significant issue in Dempster-Shafer evidence theory (DST). How to effectively counter the deception is the key problem in belief function. Limited work has been presented, of which the negation view, degree of falsity view, and the minimum conflict view are popular ones. However, previous work may suffer from combinatorial explosion or be limited to simple practical application. Based on our previous belief universal gravitation (BUG) model, a simple belief gravitational clustering (BGC) is proposed to model the evidential clustering process. Some goals, like no parameter adjustment, no initial condition, objective and unique cluster number, and robustness are achieved. Furthermore, BGC-based fusion strategy is ulteriorly raised to determine whether the evidence should be fused, which fully considers the abnormal feature of the body of evidence and the nature of the combination rule to perceive the deception. Both theoretical analysis and experimental results demonstrate the accuracy and flexibility of the proposed work.
引用
收藏
页数:12
相关论文
共 58 条
[11]   Combining belief functions based on distance of evidence [J].
Deng, Y ;
Shi, WK ;
Zhu, ZF ;
Liu, Q .
DECISION SUPPORT SYSTEMS, 2004, 38 (03) :489-493
[12]   Uncertainty measure in evidence theory [J].
Deng, Yong .
SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (11)
[13]   Deng entropy [J].
Deng, Yong .
CHAOS SOLITONS & FRACTALS, 2016, 91 :549-553
[14]  
Dubois D., 1988, Computational Intelligence, V4, P244, DOI 10.1111/j.1467-8640.1988.tb00279.x
[15]   Generalised probabilistic linguistic evidential reasoning approach for multi-criteria decision-making under uncertainty [J].
Fang, Ran ;
Liao, Huchang ;
Yang, Jian-Bo ;
Xu, Dong-Ling .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2021, 72 (01) :130-144
[16]   An Attitudinal Nonlinear Integral and Applications in Decision Making [J].
Fei, Liguo ;
Feng, Yuqiang .
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2021, 23 (02) :564-572
[17]   A Belief Coulomb Force in D-S Evidence Theory [J].
Fu, Bo ;
Fang, Jinwei ;
Zhao, Xilin ;
Chen, Xing ;
Xu, Kang ;
He, Zhangqing .
IEEE ACCESS, 2021, 9 :82979-82988
[18]   Basic probability assignment to probability distribution function based on the Shapley value approach [J].
Huang, Chongru ;
Mi, Xiangjun ;
Kang, Bingyi .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2021, 36 (08) :4210-4236
[19]   Uncertainty measurement with belief entropy on the interference effect in the quantum-like Bayesian Networks [J].
Huang, Zhiming ;
Yang, Lin ;
Jiang, Wen .
APPLIED MATHEMATICS AND COMPUTATION, 2019, 347 (417-428) :417-428
[20]  
Icard B., 2019, THESIS U PARIS SCI L