Method of Network Representation Fusion Based on D-S Evidence Theory

被引：0

作者：

Cheng X.-T. ^{[1
,2
]}

Ji L.-X. ^{[1
]}

Yin Y. ^{[1
]}

Huang R.-Y. ^{[1
]}

机构：

[1] Strategic Support Force Information Engineering University, Zhengzhou, 450002, Henan

[2] 66061 Troops of the Chinese People's Liberation Army, Beijing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 05期

关键词：

Conflict discrimination; D-S evidence theory; Feature fusion; Network representation learning;

D O I：

10.3969/j.issn.0372-2112.2020.05.004

中图分类号：

学科分类号：

摘要：

With the development of network representation learning technology, researchers are increasingly considering the integration of multi-dimensional attribute information to enhance the performance of network representation. In view of the lack of conflict discrimination and evaluation index for multi-attribute feature fusion in existing network representation learning methods, this paper proposes a network representation learning fusion method based on D-S evidence theory. Firstly, the support degree of different attribute information to the fusion result is given by SVM algorithm. Then, the fusion evaluation index in network representation learning is calculated by using evidence combination rules, and the confidence degree of each attribute is fully considered based on confusion matrix. Simulation results on three types of data sets show that the method can effectively detect conflicts in network representation fusion and improve the performance of fusion representation. © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：854 / 860

页数：6

共 19 条

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