JACIII Vol.28 No.1 pp. 186-195
doi: 10.20965/jaciii.2024.p0186

Research Paper:

ER-IVMF: Evidential Reasoning Based on Information Volume of Mass Function

Kun Mao*1 ORCID Icon, Yanni Wang*2,† ORCID Icon, Weiwei Ma*3, Jiangang Ye*4, and Wen Zhou*4

*1Faculty of Information Engineering, Quzhou College of Technology
No.18 Jiangyuan Road, Kecheng District, Quzhou 324000, China

*2Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports
No.11 West Road, North Third Ring Road, Haidian District, Beijing 100191, China

Corresponding author

*3Faculty of Business Administration, Shanxi University of Finance and Economics
No.140 Wucheng Road, Taiyuan 030006, China

*4R&D Center, Quzhou Special Equipment Inspection Center
No.592 Leyuan Road, Kecheng District, Quzhou 324000, China

May 17, 2023
September 20, 2023
January 20, 2024
evidential reasoning, uncertainty, reliability, information volume of mass function

Evidential reasoning (ER) under uncertainty is essential for various applications such as classification, prediction, and clustering. The effective realization of ER is still an open issue. Reliability plays a decisive role in the final performance as a major parameter of ER, reflecting the evidence’s inner information. This paper proposed ER based on the information volume of the mass function (ER-IVMF), which considers both weight and reliability. Numerical examples were designed to illustrate the effectiveness of the ER-IVMF. Additionally, a sports scoring system experiment was conducted to validate the superiority of the ER-IVMF. Considering the reliability based on high-order evidence information, the output of the proposed method was more accurate than that of the other methods. The experimental results proved that the proposed method was practical for addressing sports-scoring problems.

ER-IVMF, which considers both weight and reliability

ER-IVMF, which considers both weight and reliability

Cite this article as:
K. Mao, Y. Wang, W. Ma, J. Ye, and W. Zhou, “ER-IVMF: Evidential Reasoning Based on Information Volume of Mass Function,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 186-195, 2024.
Data files:
  1. [1] G. Shafer, “A mathematical theory of evidence,” Princeton University Press, 1976.
  2. [2] X. Deng, Y. Yang, and J. Yang, “A novel discrete evidence fusion approach by considering the consistency of belief structures,” Engineering Applications of Artificial Intelligence, Vol.96, Article No.103994, 2020.
  3. [3] T. Wang, X. Wei, J. Wang, T. Huang, H. Peng, X. Song, L. Valencia-Cabrera, and M. J. Perez-Jimenez, “A Weighted Corrective Fuzzy Reasoning Spiking Neural P System for Fault Diagnosis in Power Systems with Variable Topologies,” Engineering Applications of Artificial Intelligence, Vol.92, No.103680, Article No.15, 2020.
  4. [4] T. Wang, W. Liu, J. Zhao, X. Guo, and V. Terzijae, “A Rough Set-Based Bio-Inspired Fault Diagnosis Method for Electrical Substations,” Int. J. of Electrical Power & Energy Systems, Vol.119, Article No.105961, Article No.10, 2020.
  5. [5] H. Wang, Y. P. Fang, and E. Zio, “Risk Assessment of an Electrical Power System Considering the Influence of Traffic Congestion on a Hypothetical Scenario of Electrified Transportation System in New York State,” IEEE Trans. on Intelligent Transportation Systems, Vol.22, No.1, pp. 142-155, 2021.
  6. [6] F. Xiao, “EFMCDM: Evidential fuzzy multicriteria decision making based on belief entropy,” IEEE Trans. on Fuzzy Systems, Vol.28, No.7, pp. 1477-1491, 2019.
  7. [7] Z.-G. Liu, Y. Liu, J. Dezert, and F. Cuzzolin, “Evidence Combination Based on Credal Belief Redistribution for Pattern Classification,” IEEE Trans. on Fuzzy Systems, Vol.28, No.4, pp. 618-631, 2020.
  8. [8] L. Xiong, X. Su, and H. Qian, “Conflicting evidence combination from the perspective of networks,” Information Sciences, Vol.580, pp. 408-418, 2021.
  9. [9] X. Gao, X. Su, H. Qian, and X. Pan, “Dependence assessment in human reliability analysis under uncertain and dynamic situations,” Nuclear Engineering and Technology, Vol.54, No.3, pp. 948-958, 2022.
  10. [10] Z.-G. Liu, G. Qiu, G. Mercier, and Q. Pan, “A Transfer Classification Method for Heterogeneous Data Based on Evidence Theory,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.51, No.8, pp. 5129-5141, 2021.
  11. [11] J.-B. Yang and D.-L. Xu, “Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty,” IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol.32, No.3, pp. 376-393, 2002.
  12. [12] J.-B. Yang, J. Liu, J. Wang, H.-S. Sii, and H.-W. Wang, “Belief rule-base inference methodology using the evidential reasoning approach-RIMER,” IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol.36, No.2, pp. 266-285, 2006.
  13. [13] J.-B. Yang and D.-L. Xu, “Evidential reasoning rule for evidence combination,” Artificial Intelligence, Vol.205, pp. 1-29, 2013.
  14. [14] D. Meng, Z. Hu, P. Wu, S.-P. Zhu, J. A. Correia, and A. M. De Jesus, “Reliability-based optimisation for offshore structures using saddlepoint approximation,” Proc. of the Institution of Civil Engineers-Maritime Engineering, Vol.173, No.2, pp. 33-42, 2020.
  15. [15] H. Liao, Z. Ren, and R. Fang, “A Deng-Entropy-Based Evidential Reasoning Approach for Multi-Expert Multi-Criterion Decision-Making with Uncertainty,” Int. J. of Computational Intelligence Systems, Vol.13, pp. 1281-1294, 2020.
  16. [16] M. Zhou, Y.-W. Chen, X.-B. Liu, B.-Y. Cheng, and J.-B. Yang, “Weight Assignment Method for Multiple Attribute Decision Making with Dissimilarity and Conflict of Belief Distributions,” Computers & Industrial Engineering, Vol.147, Article No.106648, 2020.
  17. [17] X. Xu, J. Zheng, J.-B. Yang, D.-L. Xu, and Y.-W. Chen, “Data Classification Using Evidence Reasoning Rule,” Knowledge-Based Systems, Vol.116, pp. 144-151, 2017.
  18. [18] W. Jiang, Y. Liu, and X. Deng, “Fuzzy Entity Alignment via Knowledge Embedding with Awareness of Uncertainty Measure,” Neurocomputing, Vol.468, pp. 97-110, 2022.
  19. [19] L. Fei and Y. Wang, “An Optimization Model for Rescuer Assignments Under an Uncertain Environment by Using Dempster–Shafer Theory,” Knowledge-Based Systems, Vol.255, Article No.109680, 2022.
  20. [20] L. Pan and Y. Deng, “A New Complex Evidence Theory,” Information Sciences, Vol.608, pp. 251-261, 2022.
  21. [21] F. Xiao and W. Pedrycz, “Negation of the Quantum Mass Function for Multisource Quantum Information Fusion with its Application to Pattern Classification,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.45, No.2, pp. 2054-2070, 2023.
  22. [22] Y. Deng, “Random Permutation Set,” Int. J. of Computers Communications & Control, Vol.17, No.1, 2022.
  23. [23] J. Deng and Y. Deng, “Maximum Entropy of Random Permutation Set,” Soft Computing, Vol.26, No.21, pp. 11265-11275, 2022.
  24. [24] X. Deng and W. Jiang, “A Framework for the Fusion of Non-Exclusive and Incomplete Information on the Basis of D Number Theory,” Applied Intelligence, Vol.53, No.10, pp. 11861-11884, 2022.
  25. [25] Y. Li, F. J. Cabrerizo, E. Herrera-Viedma, and J. A. Morente-Molinera, “A Modified Uncertainty Measure of Z-numbers,” Int. J. of Computers Communications & Control, Vol.17, No.4, 2022.
  26. [26] Y. Cao, J.-B. Yang, X. Deng, and W. Jiang, “The Fusion of Discrete Z-Numbers With Application for Fault Diagnosis,” IEEE Trans. on Instrumentation and Measurement, Vol.71, 2022.
  27. [27] Y. Deng, “Information Volume of Mass Function,” Int. J. of Computers Communications & Control, Vol.15, No.6, Article No.3983, 2020.
  28. [28] Y. Song, X. Wang, W. Wu, W. Quan, and W. Huang, “Evidence Combination Based on Credibility and Non-Specificity,” Pattern Analysis & Applications, Vol.21, No.1, pp. 167-180, 2018.
  29. [29] Q. Zhou and Y. Deng, “Fractal-Based Belief Entropy,” Information Sciences, Vol.587, pp. 265-282, 2022.
  30. [30] Q. Gao, T. Wen, and Y. Deng, “Information Volume Fractal Dimension,” Fractals, Vol.29, No.08, Article No.2150263, 2021.
  31. [31] Q. Zhou and Y. Deng, “Higher Order Information Volume of Mass Function,” Information Sciences, Vol.586, pp. 501-513, 2022.
  32. [32] X. Gao, L. Pan, and Y. Deng, “A Generalized Divergence of Information Volume and its Applications,” Engineering Applications of Artificial Intelligence, Vol.108, Article No.104584, 2022.
  33. [33] D. Li and Y. Deng, “Measure Information Quality of Basic Probability Assignment: An Information Volume Method,” Applied Intelligence, Vol.52, No.10, pp. 11638-11651, 2022.
  34. [34] J. Deng and Y. Deng, “Information Volume of Fuzzy Membership Function,” Int. J. of Computers Communications & Control, Vol.16, No.1, Article No.4106, 2021.
  35. [35] F. Smarandache, J. Dezert, and J.-M. Tacnet, “Fusion of Sources of Evidence with Different Importances and Reliabilities,” 2010 13th Int. Conf. on Information Fusion, 2010.
  36. [36] L. Cholvy, L. Perrussel, and J.-M. Thévenin, “Using Inconsistency Measures for Estimating Reliability,” Int. J. of Approximate Reasoning, Vol.89, pp. 41-57, 2017.
  37. [37] A.-L. Jousselme, F. Pichon, N. Ben Abdallah, and S. Destercke, “A Note About Entropy and Inconsistency in Evidence Theory,” T. Denœux, E. Lefèvre, Z. Liu, and F. Pichon (Eds.), “Belief Functions: Theory and Applications,” pp. 215-223, Springer Int. Publishing, 2021.
  38. [38] W. Wu, Y. Song, and W. Zhao, “Evaluating Evidence Reliability on the Basis of Intuitionistic Fuzzy Sets,” Information, Vol.9, No.12, Article No.298, 2018.
  39. [39] G. Kong, D.-L. Xu, J.-B. Yang, T. Wang, and B. Jiang, “Evidential Reasoning Rule-Based Decision Support System for Predicting ICU Admission and In-Hospital Death of Trauma,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.51, No.11, pp. 7131-7142, 2021.
  40. [40] S.-W. Tang, Z.-J. Zhou, C.-H. Hu, F.-J. Zhao, and Y. Cao, “A New Evidential Reasoning Rule-Based Safety Assessment Method with Sensor Reliability for Complex Systems,” IEEE Trans. on Cybernetics, Vol.52, No.5, pp. 4027-4038, 2022.
  41. [41] X. Zhang, L. Yao, and X. Liu, “A Multi-Sensor Target Recognition Information Fusion Approach Based on Improved Evidence Reasoning Rule,” Q. Yu (Ed.), “Space Information Networks,” pp. 215-228, Springer, 2019.
  42. [42] H. Li, “Evidence Reasoning Algorithm for Multi-Criteria Decision-Making with Incomplete Attribute Weight Information,” Fire Control & Command Control, Vol.40, No.1, pp. 12-15, 2015.
  43. [43] Y.-W. Du, Y.-M. Wang, and M. Qin, “New Evidential Reasoning Rule with both Weight and Reliability for Evidence Combination,” Computers & Industrial Engineering, Vol.124, pp. 493-508, 2018.
  44. [44] A. P. Dempster, “Upper and Lower Probabilities Induced by a Multivalued Mapping,” R. R. Yager and L. Liu (Eds.), “Classic Works of the Dempster–Shafer Theory of Belief Functions,” Springer, 2008. pp. 57-72,
  45. [45] F. Smarandache and J. Dezert, “Advances and Applications of DSmT for Information Fusion. Collected Works. Volume 4,” viXra, 2015.
  46. [46] Y. Wang, “Parameter Learning for an Intuitionistic Fuzzy Belief Rule-Based Systems Based on Weight and Reliability,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 219-228, 2019.
  47. [47] C. E. Shannon, “A Mathematical Theory of Communication,” The Bell System Technical J., Vol.27, No.3, pp. 379-423, 1948.
  48. [48] Y. Deng, W. Jiang, X. Xu, Q. Li, and D. Wang, “Determining BPA Under Uncertainty Environments and its Application in Data Fusion,” J. of Electronics (China), Vol.26, No.1, pp. 13-17, 2009.

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Last updated on Feb. 19, 2024