Soft Quadratic Surface Support Vector Machine for Binary Classification

Jian Luo (), Shu-Cherng Fang (), Zhibin Deng and Xiaoling Guo ()
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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

Asia-Pacific Journal of Operational Research (APJOR), 2016, vol. 33, issue 06, 1-22

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.

Keywords: Data mining; support vector machine; binary classification; quadratic optimization; kernel-free SVM (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1142/S0217595916500469

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