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A novel method for combining conflicting evidences based on information entropy

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Abstract

Dempster-Shafer evidence theory is widely used to deal with uncertainty in intelligent systems. However, the application of this theory is constrained by the failure to balance multiple conflict evidence. The existing studies have primarily focused on investigating similarity of evidence. However, the similarity measurement is highly dependent on the capability of distance functions and will substantially increase the computational complexity. So, the efficient method with acceptable expense should be intensively investigated. In this paper, we propose a new method based on the variance of information entropy to handle the conflict of evidence. First, the fuzzy preference relations based on the variance of information entropy are constructed for multiple pieces of evidence. Next, credible values of alternative evidence are calculated. Finally, according to the Dempster’s rule of combination, the weighted average combination result can be obtained. Typical example and several actual data are used to demonstrate that the proposed method is more reasonable than some existing methods both in managing conflict and reducing computational complexity.

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Acknowledgments

The work is partially supported by National High Technology Research and Development Program of China (863 Program) (Grant No. 2013AA013801), National Natural Science Foundation of China (Grant Nos. 61174022,61573290,61503237), China State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No.BUAA-VR-14KF-02).

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Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Jin Qian, Xingfeng Guo & Yong Deng

  2. Institute of Integrated Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China

    Yong Deng

  3. Big Data Decision Institute, Jinan University, Tianhe, Guangzhou, 510632, China

    Yong Deng

  4. School of Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Yong Deng

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  1. Jin Qian
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  2. Xingfeng Guo
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Correspondence to Yong Deng.

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Qian, J., Guo, X. & Deng, Y. A novel method for combining conflicting evidences based on information entropy. Appl Intell 46, 876–888 (2017). https://doi.org/10.1007/s10489-016-0875-y

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