@inproceedings{5a3010b1a03f4b7c914aef0ace81dbb0,
title = "A New Multi-source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment",
abstract = "Dempster-Shafer theory (DST) can effectively distinguish between imprecise information and unknown information, which is widely used in information fusion. However, when the evidence highly contradicts each other, it may lead to counter-intuitive results. In addition, the existing information fusion methods do not take the negation of BPA into consideration, which can be improved. In this paper, we propose a new information fusion method by taking into account not only the information in basic probability assignment (BPA) but also the information contained in the negation of BPA. In the method, the belief divergence measure is not only used to calculate the difference between BPA and its negative BPA to reflect the information volume carried by its initial BPA, but also to calculate the difference between BPA and other BPA to consider the discrepancy between evidence. The efficiency of the method is verified by case studies.",
keywords = "Belief divergence measure, Dempster-Shafer theory, Information fusion, Negation",
author = "Hongfei Wang and Wen Jiang and Xinyang Deng and Jie Geng",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 6th International Conference on Belief Functions, BELIEF 2021 ; Conference date: 15-10-2021 Through 19-10-2021",
year = "2021",
doi = "10.1007/978-3-030-88601-1\_24",
language = "英语",
isbn = "9783030886004",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "237--246",
editor = "Thierry Den{\oe}ux and Eric Lef{\`e}vre and Zhunga Liu and Fr{\'e}d{\'e}ric Pichon",
booktitle = "Belief Functions",
}