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Soft Quadratic Surface Support Vector Machine for Binary Classification

Author

Listed:
  • Jian Luo

    (School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, P. R. China)

  • Shu-Cherng Fang

    (Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC 27695-7906, USA)

  • Zhibin Deng

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China)

  • Xiaoling Guo

    (School of Science, China University of Mining and Technology, Beijing 100083, P. R. China)

Abstract

In this paper, a kernel-free soft quadratic surface support vector machine model is proposed for binary classification directly using a quadratic function for separation. Properties (including the solvability, uniqueness and support vector representation of the optimal solution) of the proposed model are derived. Results of computational experiments on some artificial and real-world classifying data sets indicate that the proposed soft quadratic surface support vector machine model may outperform Dagher’s quadratic model and other soft support vector machine models with a Quadratic or Gaussian kernel in terms of the classification accuracy and robustness.

Suggested Citation

  • Jian Luo & Shu-Cherng Fang & Zhibin Deng & Xiaoling Guo, 2016. "Soft Quadratic Surface Support Vector Machine for Binary Classification," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(06), pages 1-22, December.
    • Handle: RePEc:wsi:apjorx:v:33:y:2016:i:06:n:s0217595916500469
    DOI: 10.1142/S0217595916500469
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    References listed on IDEAS

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    1. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    2. J. Paul Brooks, 2011. "Support Vector Machines with the Ramp Loss and the Hard Margin Loss," Operations Research, INFORMS, vol. 59(2), pages 467-479, April.
    3. Martin-Barragan, Belen & Lillo, Rosa & Romo, Juan, 2014. "Interpretable support vector machines for functional data," European Journal of Operational Research, Elsevier, vol. 232(1), pages 146-155.
    4. Yu Cao & Guangyu Wan & Fuqiang Wang, 2011. "Predicting Financial Distress Of Chinese Listed Companies Using Rough Set Theory And Support Vector Machine," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 28(01), pages 95-109.
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    Cited by:

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    2. Xin Yan & Hongmiao Zhu & Jian Luo, 2021. "A novel kernel-free nonlinear SVM for semi-supervised classification in disease diagnosis," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 948-965, November.
    3. Lin, Fengming & Fang, Shu-Cherng & Fang, Xiaolei & Gao, Zheming & Luo, Jian, 2024. "A distributionally robust chance-constrained kernel-free quadratic surface support vector machine," European Journal of Operational Research, Elsevier, vol. 316(1), pages 46-60.
    4. Luo, Jian & Hong, Tao & Gao, Zheming & Fang, Shu-Cherng, 2023. "A robust support vector regression model for electric load forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 1005-1020.
    5. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.
    6. Jian Luo & Yukai Zheng & Tao Hong & An Luo & Xueqi Yang, 2024. "Fuzzy support vector regressions for short-term load forecasting," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 363-385, September.

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