An evidence combination approach based on fuzzy discounting
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METHODOLOGIES AND APPLICATION
An evidence combination approach based on fuzzy discounting Dawei Xue1 • Yong Wang2 • Chunlan Yang1
The Author(s) 2020
Abstract In evidence theory, Dempster’s rule of combination is the most commonly applied method to aggregate bodies of evidence obtained from different sources to make a decision. However, when multiple independent bodies of evidence with conflict are aggregated by Dempster’s rule of combination, the counterintuitive results can be generated. Evidence discounting is proved to be an efficient way to eliminate the counterintuitive combination results. Following the discounting ideas, a new combination approach based on fuzzy discounting is put forward. Both the conflict between bodies of evidence and the uncertainty of a body of evidence itself are taken into account to determine the discounting factors. Jousselme’s evidence distance is used to represent conflict between bodies of evidence, and discriminability measure is defined to represent uncertainty of a body of evidence itself. Consider that both the evidence distance and the discriminability measure are semantically fuzzy. Thus, fuzzy membership functions are defined to describe both of them, and a fuzzy reasoning rule base is constructed to derive the discounting factors. Numerical examples indicate that this new combination approach proposed can achieve fast convergence speed and is robust to disturbing evidences, i.e., it is an effective method to process conflicting evidences combination. Keywords Evidence theory Evidence combination Fuzzy discounting Evidence distance Discriminability measure
1 Introduction Multisource information fusion is being more and more applied in many areas. Usually, the data acquired from various sources are uncertain or imprecise to some extent. How to properly represent and deal with the information with uncertainty is an important problem. Evidence theory (Dempster 1967; Shafer 1976), which is first proposed by Dempster and is extended to a systematic theory by Shafer, is an effective tool to model and process uncertain information. Evidence theory has become an important data fusion algorithm and is widely used in pattern recognition (Zhang et al. 2018; Yang et al. 2015; Zhang et al. 2017), fault diagnosis (Yan et al. 2019; Dong et al. 2019; Yuan et al. 2017), artificial intelligence (Han and Deng 2019; Communicated by V. Loia. & Dawei Xue [email protected] 1
School of Electronics and Electrical Engineering, Bengbu University, Bengbu 233030, China
2
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
Guo et al. 2017; Deng et al. Jan. 2019) and so on. To make a decision, multiple bodies of evidence obtained from different sources can be aggregated by combination rule. Being commutative and associative, Dempster’s rule of combination plays an important role in evidence theory and is the most commonly used combination method in practice. When applying this ru
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