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

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

Zhu Fang (School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China) and Wei Junfang (School of Resource and Material, Northeastern University at Qinhuangdao, Qinhuangdao, China)
DOI: 10.4018/japuc.2012100108
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Abstract

The performance of support vector mchine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the above deficiency, this paper proposed a parameters optimization method of support vector machine based on immune memory clone strategy (IMC). This method can solve the multi-peak model parameters selection problem better which is introduced by n-folded cross-verification. Tests on standard datasets show that this method has higher precision and faster optimization speed compared with other four methods. Then the proposed method was applied to bus passenger flow counting. The experimental results show that the method reposed in this paper obtains higher classification accuracy.
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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.

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