Conflict Management of Evidence Theory Based on Belief Entropy and Negation

被引:18
|
作者
Li, Shanshan [1 ]
Xiao, Fuyuan [1 ]
Abawajy, Jemal H. [2 ]
机构
[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China
[2] Deakin Univ, Sch Informat Technol, Burwood, Vic 3220, Australia
来源
IEEE ACCESS | 2020年 / 8卷
关键词
Entropy; Uncertainty; Reliability; Measurement uncertainty; Probability distribution; Education; Target recognition; Dempster-Shafer evidence theory; conflict management; discount coefficient; Deng entropy; negation; target recognition; INTUITIONISTIC FUZZY-SETS; DECISION-MAKING; FAILURE MODE; DIVERGENCE MEASURE; REASONING APPROACH; COMBINATION; RULE; UNCERTAINTY; FUZZINESS; FRAMEWORK;
D O I
10.1109/ACCESS.2020.2975802
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Discount coefficient is an efficient method to address conflicting evidence combination in Dempster-Shafer evidence theory. However, how to determine the discount coefficient of each evidence is an open issue. In this paper, considering both the influence of the amount of information contained in the evidence itself and the fuzziness of the evidence based on the negation of basic belief assignment, a new discount coefficient is presented. The proposed discount coefficient is a fractional form. The numerator is Deng entropy, and the denominator is entropy difference between initial body of evidence (BOE) and its negation. The more information contained in the evidence, the more value is obtained. And the lower fuzziness of evidence, the less value is obtained. A numerical example is given to illustrate the application of this proposed method in the combination of highly conflicting evidence.
引用
收藏
页码:37766 / 37774
页数:9
相关论文
共 50 条
  • [41] A new classification method based on the negation of a basic probability assignment in the evidence theory
    Wu, Dongdong
    Liu, Zijing
    Tang, Yongchuan
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 96
  • [42] A new classification method based on the negation of a basic probability assignment in the evidence theory
    Wu, Dongdong
    Liu, Zijing
    Tang, Yongchuan
    Engineering Applications of Artificial Intelligence, 2020, 96
  • [43] Negation and entropy: Effectual knowledge management equipment for learning organizations
    Anjaria, Kushal
    EXPERT SYSTEMS WITH APPLICATIONS, 2020, 157
  • [44] Management evaluation of enterprise system based on management entropy theory
    Jing, Yongchun
    Luo, Yuyan
    Ren, Peiyu
    Zhou, Ming
    INFORMATION SCIENCE AND MANAGEMENT ENGINEERING, VOLS 1-3, 2014, 46 : 2163 - 2167
  • [45] Research on College Student Interpersonal Conflict Management based on the Conflict Theory
    Li, Zhonghua
    Shan, Weifeng
    PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON INFORMATION, BUSINESS AND EDUCATION TECHNOLOGY (ICIBET 2013), 2013, 26 : 1184 - 1188
  • [46] An Improved Method to Manage Conflict Data Using Elementary Belief Assignment Function in the Evidence Theory
    Li, Rongfei
    Li, Hao
    Tang, Yongchuan
    IEEE ACCESS, 2020, 8 : 37926 - 37932
  • [47] Conflict evidence fusion method based on Lance distance and credibility entropy
    Wang X.
    Di P.
    Yin D.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2022, 44 (02): : 592 - 602
  • [48] Conflict Evidence Fusion Algorithm Based on Cosine Distance and Information Entropy
    Chen, Ziyang
    Zhang, Yang
    Journal of Computers (Taiwan), 2023, 34 (03) : 343 - 355
  • [49] Uncertainty measure based on Tsallis entropy in evidence theory
    Gao, Xiaozhuan
    Liu, Fan
    Pan, Lipeng
    Deng, Yong
    Tsai, Sang-Bing
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2019, 34 (11) : 3105 - 3120
  • [50] Fault diagnosis of rotating machinery based on evidence theory of evidence entropy
    Zhang, Ping
    Zhang, Xiaodong
    Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2010, 30 (01): : 55 - 58