The Combination Method of Conflict Evidence Based on Classification Correction

被引:0
作者
Yang, Kehua [1 ]
Feng, Yuping [1 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China
来源
2016 INTERNATIONAL CONFERENCE ON NETWORK AND INFORMATION SYSTEMS FOR COMPUTERS (ICNISC) | 2016年
关键词
data fusion; evidence theory; evidence classification; evidence distance; clustering analysis;
D O I
10.1109/ICNISC.2016.52
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to efficiently combine highly conflict evidence, a new method of evidence combination was presented. Firstly, since traditional evidence distance can't measure the degree of similarity between evidence effectively in some cases, the paper analyzed traditional evidence distance functions and combined with Jousselme distance, then presented a new evidence distance. And then, reliability of each evidence was obtained to determine relative reliability and weight factors, according to the new evidence distance. Secondly, evidence was classified into three categories: consistent evidence, non-conflict evidence and conflict evidence, according to the new evidence distance parameter and the local conflict parameter. Finally, different evidence was revised in different ways by using relative reliability and weight factors, then revised evidence was combined by means of Dempster's combination rule. A numerical example shows that the proposed method can combine highly conflict evidence efficiently and accelerates convergence.
引用
收藏
页码:213 / 217
页数:5
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