Artificial immune system based on adaptive clonal selection for feature selection and parameters optimisation of support vector machines

作     者：Hashemipour, Maryam Sadat Soleimani, Seyed Ali 

作者机构：Univ Shahrood Dept Elect & Robot Engn Shahrood Iran 

出 版 物：《CONNECTION SCIENCE》 (连接科学)

年 卷 期：2016年第28卷第1期

页      面：47-62页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：support vector machine Adaptive clonal selection algorithm feature selection artificial immune system algorithm 

摘      要：Artificial immune system (AIS) algorithm based on clonal selection method can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure along with the feature selection significantly impacts on the classification accuracy rate. In this study, AIS based on Adaptive Clonal Selection (AISACS) algorithm has been used to optimise the SVM parameters and feature subset selection without degrading the SVM classification accuracy. Several public datasets of University of California Irvine machine learning (UCI) repository are employed to calculate the classification accuracy rate in order to evaluate the AISACS approach then it was compared with grid search algorithm and Genetic Algorithm (GA) approach. The experimental results show that the feature reduction rate and running time of the AISACS approach are better than the GA approach.