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A new correlation belief function in Dempster-Shafer evidence theory and its application in classification
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Tang, Yongchuan, Zhang, Xu, Zhou, Ying, Huang, Yubo and Zhou, Deyun (2023) A new correlation belief function in Dempster-Shafer evidence theory and its application in classification. Scientific Reports, 13 (1). 7609. doi:10.1038/s41598-023-34577-y ISSN 2045-2322.
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Official URL: https://doi.org/10.1038/s41598-023-34577-y
Abstract
Uncertain information processing is a key problem in classification. Dempster-Shafer evidence theory (D-S evidence theory) is widely used in uncertain information modelling and fusion. For uncertain information fusion, the Dempster’s combination rule in D-S evidence theory has limitation in some cases that it may cause counterintuitive fusion results. In this paper, a new correlation belief function is proposed to address this problem. The proposed method transfers the belief from a certain proposition to other related propositions to avoid the loss of information while doing information fusion, which can effectively solve the problem of conflict management in D-S evidence theory. The experimental results of classification on the UCI dataset show that the proposed method not only assigns a higher belief to the correct propositions than other methods, but also expresses the conflict among the data apparently. The robustness and superiority of the proposed method in classification are verified through experiments on different datasets with varying proportion of training set.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
SWORD Depositor: | Library Publications Router | |||||||||
Library of Congress Subject Headings (LCSH): | Pattern recognition systems , Expert systems (Computer science), Fuzzy expert systems, Neural networks (Computer science) | |||||||||
Journal or Publication Title: | Scientific Reports | |||||||||
Publisher: | Nature Publishing Group | |||||||||
ISSN: | 2045-2322 | |||||||||
Official Date: | 10 May 2023 | |||||||||
Dates: |
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Volume: | 13 | |||||||||
Number: | 1 | |||||||||
Article Number: | 7609 | |||||||||
DOI: | 10.1038/s41598-023-34577-y | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 6 June 2023 | |||||||||
Date of first compliant Open Access: | 7 June 2023 | |||||||||
RIOXX Funder/Project Grant: |
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