Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM

• Published in: Applied soft computing Vol. 109; p. 107541
• Main Authors: Huang, Wencheng, Liu, Hongyi, Zhang, Yue, Mi, Rongwei, Tong, Chuangui, Xiao, Wei, Shuai, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: GA-SVM; GS-SVM; Kernel function; PSO-SVM; Railway dangerous goods transportation system; Risk identification; Kernel function; PSO-SVM; Risk identification; GA-SVM; GS-SVM; Railway dangerous goods transportation system

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search). The three improved SVM approaches are applied to identify the r...