Combining conflicting evidence based on Pearson correlation coefficient and weighted graph

International Journal of Intelligent SystemsVolume 36, Issue 12 p. 7443-7460 RESEARCH ARTICLE Combining conflicting evidence based on Pearson correlation coefficient and weighted graph Jixiang Deng, Jixiang Deng orcid.org/0000-0002-1521-1770 Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorYong Deng, Corresponding Author Yong Deng dengentropy@uestc.edu.cn orcid.org/0000-0001-9286-2123 Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, China School of Education, Shannxi Normal University, Xi'an, China School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland Correspondence Yong Deng, Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, 610054 Chengdu, China. Email: dengentropy@uestc.edu.cn Kang Hao Cheong, Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore S487372, Singapore. Email: kanghao_cheong@sutd.edu.sgSearch for more papers by this authorKang Hao Cheong, Corresponding Author Kang Hao Cheong kanghao_cheong@sutd.edu.sg orcid.org/0000-0002-4475-5451 Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), Singapore, Singapore SUTD-Massachusetts Institute of Technology International Design Centre, Singapore, Singapore Correspondence Yong Deng, Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, 610054 Chengdu, China. Email: dengentropy@uestc.edu.cn Kang Hao Cheong, Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore S487372, Singapore. Email: kanghao_cheong@sutd.edu.sgSearch for more papers by this author Jixiang Deng, Jixiang Deng orcid.org/0000-0002-1521-1770 Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, ChinaSearch for more papers by this authorYong Deng, Corresponding Author Yong Deng dengentropy@uestc.edu.cn orcid.org/0000-0001-9286-2123 Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, China School of Education, Shannxi Normal University, Xi'an, China School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland Correspondence Yong Deng, Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, 610054 Chengdu, China. Email: dengentropy@uestc.edu.cn Kang Hao Cheong, Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore S487372, Singapore. Email: kanghao_cheong@sutd.edu.sgSearch for more papers by this authorKang Hao Cheong, Corresponding Author Kang Hao Cheong kanghao_cheong@sutd.edu.sg orcid.org/0000-0002-4475-5451 Science, Mathematics and Technology Cluster, Singapore University of Technology and Design (SUTD), Singapore, Singapore SUTD-Massachusetts Institute of Technology International Design Centre, Singapore, Singapore Correspondence Yong Deng, Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, 610054 Chengdu, China. Email: dengentropy@uestc.edu.cn Kang Hao Cheong, Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore S487372, Singapore. Email: kanghao_cheong@sutd.edu.sgSearch for more papers by this author First published: 05 August 2021 https://doi.org/10.1002/int.22593Citations: 13Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Dempster–Shafer evidence theory (evidence theory) has been widely used as an efficient method for dealing with uncertainty. In evidence theory, Dempster's rule is the most well-known evidence combination method but it does not work well when the evidence is in high conflict. To improve the performance of combining conflicting evidence, an original and novel evidence combination method is presented based on the Pearson correlation coefficient and weighted graph. The proposed method can correctly recognize the alternative situation with a high accuracy. Besides, the convergence performance of this method is better when compared with other combination rules. In addition, the weighted graph generated by the proposed method can directly represent the relationship between different evidence, which can help researchers estimate the reliability of different body of evidence. Our experimental results indicate the advantages of our proposed evidence combination rule over existing methods, and the results are analyzed and discussed. Citing Literature Volume36, Issue12December 2021Pages 7443-7460 RelatedInformation