Multi-Class Support Vector Data Description Based on Evidence Theory

被引：0
|
作者：
Zhang S. [1 ]
Han D. [1 ]
Fan X. [1 ]
机构：
[1] Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an
来源：
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | 2023年 / 57卷 / 02期
关键词：
belief functions; evidence theory; multiple attribute decision making; support vector data description;
D O I：
10.7652/xjtuxb202302016
中图分类号：
学科分类号：
摘要：
Original multi-class support vector data description (SVDD) algorithm and its extension algorithm ignore the differences among hyperspheres and fail to make full use of the output information of hyperspheres. To address these problems, a multi-class support vector data description algorithm based on evidence theory (evidential multi-class SVDD algorithm) is proposed. Firstly, a hypersphere is trained for each class of samples, and the accuracy and closeness of each hypersphere are calculated. Secondly, the accuracy and closeness obtained in the previous step are used to calculate the reliability of hypersphere. After that, the output information and reliability of hypersphere are used to calculate the belief functions of samples, which are generated by two methods: the triple focal element method and the method based on payoff matrix. In the end, Dempster combination rule is used to fuse the belief functions, and Pignistic method is used to convert the fused belief functions into probability for final decision. Extensive experimental results on two artificial datasets and multiple UCI datasets show that the evidential multi-class SVDD algorithm achieves better classification performance than the traditional algorithm. Simulation results on multiple datasets show that the evidential multi-class SVDD algorithm has a 3% improvement in accuracy compared with the traditional multi-class SVDD algorithm. © 2023 Xi'an Jiaotong University. All rights reserved.
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页码：151 / 160
页数：9
相关论文
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