Journal
IEEE ACCESS
Volume 6, Issue -, Pages 71839-71845Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2882545
Keywords
Dempster-Shafer evidence theory; dependent evidence; information fusion; mutual information
Categories
Funding
- National Natural Science Foundation of China [61503237, 61573290]
- Shanghai Education Development Foundation
- Shanghai Municipal Education Commission, Shanghai Science and Technology Committee Key Program [18020500900, 15160500800]
- Shanghai Key Laboratory of Power Station Automation Technology [13DZ2273800]
- Shanghai Education Commission Excellent Youth Project [ZZsdl15144]
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The Dempster-Shafer evidence theory has been investigated for many applications due to its ability in handling uncertainty and ignorance. However, the classical Dempster's combination rule can only be applied to the cases, where evidence is independent. This assumption is often unrealistic and may lead to unreasonable decisions. In this paper, a new method for combining dependent evidence based on mutual information is proposed. First, the mutual information is used to measure the dependence degree between evidence. Second, the total discount coefficient is defined based on the dependence degree between evidence. Finally, the aggregation model based on the total discount coefficient and Dempster's combination rule is presented in the information fusion stage. The experiments on Iris data is illustrated to show the use and effectiveness of the proposed method. Compared with other methods, the proposed model has the highest classification recognition accuracy.
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