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A New Methodology to Arrive at Membership Weights for Fuzzy SVM
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Author(s): Maruthamuthu A. (National Institute of Technology, Tiruchirappalli, India), Punniyamoorthy Murugesan (National Institute of Technology, Tiruchirappalli, India)and Muthulakshmi A. N. (National Institute of Technology, Tiruchirappalli, India)
Copyright: 2022
Volume: 11
Issue: 1
Pages: 15
Source title:
International Journal of Fuzzy System Applications (IJFSA)
Editor(s)-in-Chief: Deng-Feng Li (School of Management and Economics, University of Electronic Science and Technology of China (UESTC))
DOI: 10.4018/IJFSA.285556
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Abstract
Support Vector Machine (SVM) is a supervised classification technique that uses the regularization parameter and Kernel function in deciding the best hyperplane to classify the data. SVM is sensitive to outliers, and it makes the model weak. To overcome the issue, the Fuzzy Support Vector Machine (FSVM) introduces fuzzy membership weight into its objective function, which focuses on grouping the fuzzy data points accurately. Knowing the importance of the membership weights in FSVM, we have introduced four new expressions to compute the FSVM membership weights in this study. They are determined from the Fuzzy C-means Algorithm's membership values (FCM). The performances of FSVM with three different kernels are assessed in terms of accuracy. The experiments are conducted for various combinations of FSVM parameters, and the best combinations for each kernel are highlighted. Six benchmark datasets are used to demonstrate the performance of FSVM and the proposed models’ performance are compared with the existing models in recent literature.
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