SVM Parameter Optimization based on Immune Memory Clone Strategy and Application in Bus Passenger Flow Counting

Introduction

The Support Vector Machine (SVM) is a new machine learning method that based on the Statistic Learning Theory (SLT) (Zhou, Yang, 2006; Bo, Yuchun, Yang-Qing, Chung-Dar, & Weber, 2005). The selection quality of SVM parameters and kernel functions has an effect on the learning and generation performance. In order to find the best parameters for SVM, many researchers have done a mass of study. The parameters in SVM are usually selected by man’s experience, such as n-folded cross-verification (Nello & John, 2006). Recently, there are some automatic parameter selection methods researched such as colony algorithm and genetic algorithm (Chunxiu, Huiren, & Chunxia, 2010; Xiangying, Huiyan, & Fengzhen, 2010; Ning, Zhigang, & Qi, 2009; Yuan & Guangchen, 2010). These methods are efficient and automatic for optimizing parameters in a certain degree. But they depend on optimization model construction, and convergence to local optimum sometimes. According to these problems, a parameters optimization method of SVM based on immune memory clone strategy (IMC) is proposed in this paper. The results of experiment show that the proposed method has more efficiency of optimization and higher accuracy rate of classification than other existent methods.