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A Novel Parameter Optimization Algorithm Based on Immune Memory Clone Strategy

Author

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  • Zhu Fang

    (School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China)

  • Wei Junfang

    (School of Resource and Material, Northeastern University at Qinhuangdao, Qinhuangdao, China)

Abstract

The performance of support vector machine (SVM) depends on the selection of model parameters, however, the selection of SVM model parameters more depends on the empirical value. According to the deficiency, this paper proposes 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. 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.

Suggested Citation

  • Zhu Fang & Wei Junfang, 2012. "A Novel Parameter Optimization Algorithm Based on Immune Memory Clone Strategy," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), IGI Global, vol. 4(3), pages 102-108, July.
    • Handle: RePEc:igg:japuc0:v:4:y:2012:i:3:p:102-108
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