Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (10): 2692-2700.doi: 10.23940/ijpe.19.10.p15.26922700

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Using Non-Subjective Approximation Algorithm of D-S Evidence Theory for Improving Data Fusion

Ning Zhangab, Peng Chenc, Kai Hec, Zhao Lic, and Xiaosheng Yuc*   

  1. aRemote Sensing Application Center, Ministry of Housing and Urban-Rural Development of the People's Republic of China Beijing, 100835, China
    bInstitute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
    cCollege of Computer and Information, Three Gorges University, Yichang, 443002, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: Yu Xiaosheng
  • About author:

    * Corresponding author. E-mail address:

  • Supported by:
    This work is supported by the National Key Research and Development Program of China (No 2016YFC0802500) and the National Natural Science Foundation of China (No 61272236)


The paper efficiently processes the issue of "focal element explosion" produced when many focal elements are fused according to D-S evidence theory. The effectiveness of subjective approximation algorithms is low since they heavily involve artificial participation. In addition, the accuracy of the results calculated by the non-subjective approximation algorithm is better. In this paper, a non-subjective approximation algorithm based on evidence levels is proposed to address the above-mentioned problem. First, the evidence level is mainly determined by the cumulative mass value of the main focal element, and the number of focal elements is reduced by approximate treatment according to the corresponding initial standard determined by the levels of evidence. Second, to further increase the accuracy of the results, the levels of evidence are used to determine the order of fusion and the discounts of evidence. It obvious that even if there is erroneous or uncertain evidence in the fused evidence, it will not affect the results significantly. The experimental results show that the algorithm outperforms others in terms of adaptability and accuracy.

Key words: data fusion, approximation algorithm, evidence theory