Twin support vector machines based on chaotic mapping dung beetle optimization algorithm

被引：9

作者：

Huang, Huajuan ^{[1
]}

Yao, Zhenhua ^{[1
]}

Wei, Xiuxi ^{[1
,2
]}

Zhou, Yongquan ^{[1
,3
]}

机构：

[1] Guangxi Minzu Univ, Coll Artificial Intelligence, Nanning 530006, Peoples R China

[2] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China

[3] Guangxi Minzu Univ, Guangxi Key Lab Hybrid Computat & IC Design Anal, Nanning 530006, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING | 2024年 / 11卷 / 03期

基金：

中国国家自然科学基金;

关键词：

twin support vector machines; dung beetle optimization algorithm; parameter selection; chaotic mapping; classification; SVM; CLASSIFICATION;

D O I：

10.1093/jcde/qwae040

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Twin Support Vector Machine (TSVM) is a powerful machine learning method that is usually used to solve binary classification problems. But although the classification speed and performance of TSVM is better than that of primitive support vector machine, TSVM still faces the problem of difficult parameter selection; therefore, to overcome the problem of parameter selection of TSVM, this paper proposes a Chaotic Mapping Dung Beetle Optimization Algorithm-based Twin Support Vector Machine (CMDBO-TSVM) for automatic parameter selection. Due to the uncertainty of the random initialization population of the original Dung Beetle Optimization Algorithm, this paper additionally adds chaotic mapping initialization to improve the Dung Beetle Optimization Algorithm. Experiments on the dataset through this paper show that the classification accuracy of the CMDBO-TSVM has a better performance. Graphical Abstract

引用

页码：101 / 110

页数：10

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