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A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

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Listed:
  • Lei Chen
  • Ling Diao
  • Jun Sang

Abstract

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.

Suggested Citation

  • Lei Chen & Ling Diao & Jun Sang, 2019. "A novel weighted evidence combination rule based on improved entropy function with a diagnosis application," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:1:p:1550147718823990
    DOI: 10.1177/1550147718823990
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    References listed on IDEAS

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    1. Xiaoyan Su & Sankaran Mahadevan & Peida Xu & Yong Deng, 2015. "Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1296-1316, July.
    2. Yong Deng & Yang Liu & Deyun Zhou, 2015. "An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-6, October.
    3. Lei Chen & Ling Diao & Jun Sang, 2018. "Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, January.
    4. Deyun Zhou & Yongchuan Tang & Wen Jiang, 2017. "A modified belief entropy in Dempster-Shafer framework," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    5. Beynon, Malcolm & Curry, Bruce & Morgan, Peter, 2000. "The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling," Omega, Elsevier, vol. 28(1), pages 37-50, February.
    6. Du, Wen-Bo & Gao, Yang & Liu, Chen & Zheng, Zheng & Wang, Zhen, 2015. "Adequate is better: particle swarm optimization with limited-information," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 832-838.
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    Citations

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    Cited by:

    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Shijun Xu & Yi Hou & Xinpu Deng & Peibo Chen & Kewei Ouyang & Ye Zhang, 2021. "A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.
    3. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Yu Zhang & Wen Jiang & Xinyang Deng, 2019. "Fault diagnosis method based on time domain weighted data aggregation and information fusion," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.

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