Data Fusion Method Based on Improved D-S Evidence Theory

被引:16
作者
Zhang, Wei [1 ]
Ji, Xilin [2 ]
Yang, Yang [1 ]
Chen, Jianwen [3 ]
Gao, Zhipeng [1 ]
Qiu, Xuesong [1 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing, Peoples R China
[2] Elect Equipment Syst Engn Corp Inst China, Beijing, Peoples R China
[3] Beijing Informat & Commun Technol Res Ctr, Beijing, Peoples R China
来源
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) | 2018年
关键词
data fusion; D-S evidence theory; evidence distance; MULTISENSOR INFORMATION FUSION; DEMPSTER-SHAFER THEORY;
D O I
10.1109/BigComp.2018.00145
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
As the rapid growth of user-generated data from social networks, wilds and social tagging systems, it is necessary to understand the high-level semantics and user subjective perceptions from such a large volume of data. In the era of big data flooding, how to fuse the emotional computing results from massive data to obtain effective conclusions and decisions has become a problem. This paper combines D-S evidence theory with data fusion and effectively solves the conflict of evidence evidence in D-S evidence theory by introducing the Bhattacharyya distance, the confidence level of evidence and the modified combination rule. The experimental results show that the improved data fusion method can get the data fusion result, and the result has a high accuracy and credibility.
引用
收藏
页码:760 / 766
页数:7
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