A Modified Evidence Theory Model for Data Fusion

被引：0

作者：

Liu, Jie ^{[1
,3
]}

Liu, Dayou ^{[1
,3
]}

Wang, Xinhua ^{[1
,2
]}

Ni, Ni ^{[4
]}

Zhu, Yungang ^{[1
,3
]}

机构：

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun, Jilin, Peoples R China

[2] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, State Key Lab Appl Opt, Changchun, Jilin, Peoples R China

[3] Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun, Jilin, Peoples R China

[4] Bejing Entry Exit Inspect & Quarantine Bur, Beijing, Peoples R China

来源：

2017 2ND IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2017) | 2017年

基金：

中国博士后科学基金;

关键词：

dempster-shafer theory; Gaussian function; convex function;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To overcome the drawbacks of traditional convex evidence, in this paper we proposed a modified convex evidence theory model, we presented the modified combination function and use it to combine mass function of ordered propositions, we present the calculation of the parameters of the proposed combination function, and proposed a more accurate method to find the proposition which is most likely true. The theoretical analysis and experimental results demonstrate that the proposed method has higher accuracy than traditional convex evidence.

引用

页码：39 / 43

页数：5

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