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

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

Received:
May 17, 2023
Accepted:
September 20, 2023
Published:
January 20, 2024
Keywords:
evidential reasoning, uncertainty, reliability, information volume of mass function
Abstract

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.
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