Ordinal belief entropy

被引:1
|
作者
He, Yuanpeng [1 ]
Deng, Yong [1 ]
机构
[1] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Ordinal belief entropy; Uncertainty; Sequence; INTUITIONISTIC FUZZY-SETS; PERSPECTIVE;
D O I
10.1007/s00500-023-07947-x
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Entropies are widely applied in measuring the degree of uncertainties existing in frame of discernment. However, all of these entropies regard the frame as a whole that has already been determined, which does not conform to actual situations. In real life, everything comes in a sequence. So, how to measure uncertainties of the dynamic process of determining sequence of propositions contained in a frame of discernment is still an open issue, and no related research has been proceeded. Therefore, a novel ordinal entropy to measure uncertainty of frame of discernment considering the order of propositions is proposed in this paper. Compared with other traditional entropies, it manifests effects on degree of uncertainty brought by orders of propositions. For example, assume there exist three propositions, for ordinal belief entropy, the potential categories of situations are C-3(1) C-2(1) C-1(1), which illustrates that the proposed entropy is able to measure more complex environment and more matches actual circumstances. But for other entropies, they have only one certain value for descriptions of actual situations and the ability of measuring environment is limited. In a general rule, if number of propositions is n, then there are Pi(n)(k)=1 C-k(1) categories of description of situations with respect to ordinal belief entropy. Besides, some numerical examples are provided to verify the correctness and validity of the proposed entropy in this paper.
引用
收藏
页码:6973 / 6981
页数:9
相关论文
共 50 条
  • [21] Kolmogorov-Sinai entropy from the ordinal viewpoint
    Keller, Karsten
    Sinn, Mathieu
    PHYSICA D-NONLINEAR PHENOMENA, 2010, 239 (12) : 997 - 1000
  • [22] ENTROPY DETERMINATION BASED ON THE ORDINAL STRUCTURE OF A DYNAMICAL SYSTEM
    Keller, Karsten
    Maksymenko, Sergiy
    Stolz, Inga
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, 2015, 20 (10): : 3507 - 3524
  • [23] Generating Multivariate Ordinal Data via Entropy Principles
    Lee, Yen
    Kaplan, David
    PSYCHOMETRIKA, 2018, 83 (01) : 156 - 181
  • [24] The ordinal Kolmogorov-Sinai entropy: A generalized approximation
    Fouda, J. S. Armand Eyebe
    Koepf, Wolfram
    Jacquir, Sabir
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2017, 46 : 103 - 115
  • [25] ORDINAL DECISION TREES BASED ON FUZZY RANK ENTROPY
    Wang, Xin
    Zhai, Junhai
    Chen, Jiankai
    Wang, Xizhao
    PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR), 2015, : 208 - 213
  • [26] Generating Multivariate Ordinal Data via Entropy Principles
    Yen Lee
    David Kaplan
    Psychometrika, 2018, 83 : 156 - 181
  • [27] Estimating topological entropy using ordinal partition networks
    Sakellariou, Konstantinos
    Stemler, Thomas
    Small, Michael
    PHYSICAL REVIEW E, 2021, 103 (02)
  • [28] On strengthening the logic of iterated belief revision: Proper ordinal interval operators
    Booth, Richard
    Chandler, Jake
    ARTIFICIAL INTELLIGENCE, 2020, 285 : CP3 - U33
  • [29] On Strengthening the Logic of Iterated Belief Revision: Proper Ordinal Interval Operators
    Booth, Richard
    Chandler, Jake
    SIXTEENTH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, 2018, : 210 - 219
  • [30] Belief revision and information fusion on optimum entropy
    Kern-Isberner, G
    Rödder, W
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2004, 19 (09) : 837 - 857