Twin Support Vector Machines Based on Chaotic Mapping Dung Beetle Optimization Algorithm

Abstract

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 Twin Support Vector Machine is better than that of primitive Support Vector Machine, Twin Support Vector Machine still faces the problem of difficult parameter selection, therefore, to overcome the problem of parameter selection of Twin Support Vector Machine, 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 twin support vector machine based on the chaotic mapping dung beetle optimization algorithm has a better performance.