Super-parameter selection for Gaussian-Kernel SVM based on outlier-resisting |
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| Affiliation: | 1. Electrical Engineering Department, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha 768018, India;2. Department of Applied Physics, University of Calcutta, 92 APC Road, Kolkata, West Bengal 700009, India;1. Dept. of Electronics and Multimedia Communications, FEI Technical University of Košice, Slovak Republic;2. Budapest University of Technology and Economics, Dept. of Measurement, Budapest, Hungary;1. Department of Mechanical Engineering, University of Alaska Anchorage, ENGR Building, Rm. 201 3211 Providence Dr, Anchorage, AK, United States;2. Department of Electrical Engineering, University of Alaska Anchorage, ENGR Building, Rm. 201 3211 Providence Dr, Anchorage, AK, United States;1. School of Civil Engineering, Dalian University of Technology, Dalian 116024, PR China;2. School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, PR China;1. University of North Carolina Chapel Hill Project-China, Guangzhou 510095, China;2. University of North Carolina Chapel Hill, Institute of Global Health and Infectious Diseases, Chapel Hill, NC, USA;3. Division of Health Sciences, Bioethics Centre, University of Otago, Dunedin, New Zealand;4. School of Sociology and Anthropology, Sun Yat-sen University, Guangzhou, China;5. Center for Applied Ethics and Department of Social Sciences, Fudan University, Shanghai, China;6. Harvard Asia Center, Harvard University, Cambridge, MA, USA |
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| Abstract: | The learning ability and generalizing performance of the support vector machine (SVM) mainly relies on the reasonable selection of super-parameters. When the scale of the training sample set is large and the parameter space is huge, the existing popular super-parameter selection methods are impractical due to high computational complexity. In this paper, a novel super-parameter selection method for SVM with a Gaussian kernel is proposed, which can be divided into the following two stages. The first one is choosing the kernel parameter to ensure a sufficiently large number of potential support vectors retained in the training sample set. The second one is screening out outliers from the training sample set by assigning a special value to the penalty factor, and training out the optimal penalty factor from the remained training sample set without outliers. The whole process of super-parameter selection only needs two train-validate cycles. Therefore, the computational complexity of our method is low. The comparative experimental results concerning 8 benchmark datasets show that our method possesses high classification accuracy and desirable training time. |
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| Keywords: | Support vector machine Super-parameter selection Outlier-resisting Classification accuracy Computational complexity |
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