Optimal Combination of SVM and Bayesian Density Model Using Dempster-Shafer Theory

被引：1

作者：

Zhang, Chenbin ^{[1
]}

Qin, Ningning ^{[2
]}

Yang, Le ^{[3
]}

机构：

[1] Jiangnan Univ, Dept Internet Things, LiHu Rd 1800, Wuxi, Jiangsu, Peoples R China

[2] Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, LiHu Rd 1800, Wuxi, Jiangsu, Peoples R China

[3] Univ Canterbury, Dept Elect & Comp Engn, Private Bag 4800, Christchurch 8140, New Zealand

来源：

ICMLC 2020: 2020 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING | 2018年

关键词：

Classifier Fusion; SVM; Bayesian Density Model; Dempster-Shafer Theory;

D O I：

10.1145/3383972.3383987

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In pattern classification, the diversity among classifiers is known to be able to provide complementary knowledge and improve classification performance, if properly exploited. In this paper, we propose to integrate the support vector machine (SVM) and the Bayesian density model by utilizing their respective posterior classification probabilities. The Dempster-Shafer (DS) theory was adopted for fusing the two classifiers. The effectiveness of the proposed method was verified using three datasets. The performance of the proposed approach was shown to be superior to that of the benchmark methods.

引用

页码：505 / 509

页数：5

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