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Mohammadi, M., Sarmad, M.. (1398). Robustified distance based fuzzy membership function for support vector machine classification. نشریه علمی دانشگاه سیستان و بلوچستان, 16(6), 191-204. doi: 10.22111/ijfs.2019.5028
M. Mohammadi; M. Sarmad. "Robustified distance based fuzzy membership function for support vector machine classification". نشریه علمی دانشگاه سیستان و بلوچستان, 16, 6, 1398, 191-204. doi: 10.22111/ijfs.2019.5028
Mohammadi, M., Sarmad, M.. (1398). 'Robustified distance based fuzzy membership function for support vector machine classification', نشریه علمی دانشگاه سیستان و بلوچستان, 16(6), pp. 191-204. doi: 10.22111/ijfs.2019.5028
Mohammadi, M., Sarmad, M.. Robustified distance based fuzzy membership function for support vector machine classification. نشریه علمی دانشگاه سیستان و بلوچستان, 1398; 16(6): 191-204. doi: 10.22111/ijfs.2019.5028

Robustified distance based fuzzy membership function for support vector machine classification

Iranian Journal of Fuzzy Systems
مقاله 15، دوره 16، شماره 6، بهمن و اسفند 2019، صفحه 191-204    اصل مقاله (4.82 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2019.5028
نویسندگان
M. Mohammadi* ؛ M. Sarmad
Department of Statistics, Ferdowsi University of Mashhad, Iran
چکیده
Fuzzification of support vector machine has been utilized to deal with outlier and noise problem. This importance is achieved, by the means of fuzzy membership function, which is generally built based on the distance of the points to the class centroid. The focus of this research is twofold. Firstly, by taking the advantage of robust statistics in the fuzzy SVM, more emphasis on reducing the impact of outliers on the generalizability of SVM has been placed. Moreover, the variety of membership function for the elliptical data has been designated, based on the classic and robust Mahalanobis distance. Minimum covariance determinant and orthogonalised Gnanadesikan Kettenring estimators are employed in the structure of the robust--fuzzy SVM.
By implementing the new membership function, the disadvantages of the traditional fuzzy membership function has been rectified. Simulated and real benchmarking data set confirm the effectiveness of the proposed methods. Compared with the traditional SVM and fuzzy SVM, these methods give a better performance on reducing the effects of outliers and significantly improves the classification accuracy and generalization.
کلیدواژه‌ها
Support vector machine؛ Noise/outlier؛ Robust statistics؛ Fuzzy membership function؛ Minimum covariance determinant estimator؛ Orthogonalised Gnanadesikan Kettenring estimator
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