An intuitionistic fuzzy set based S$$^$$VM model for binary classification with mislabeled information |
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| Authors: | Ye Tian Zhibin Deng Jian Luo Yueqing Li |
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| Affiliation: | 1.School of Business Administration and Research Center of Big Data,Southwestern University of Finance and Economics,Chengdu,China;2.School of Economics and Management,University of Chinese Academy of Sciences,Beijing,China;3.Key Laboratory of Big Data Mining and Knowledge Management,Chinese Academy of Sciences,Beijing,China;4.School of Management Science and Engineering,Dongbei University of Finance and Economics,Dalian,China;5.Department of Industrial Engineering,Lamar University,Beaumont,USA |
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| Abstract: | Traditionally, robust and fuzzy support vector machine models are used to handle the binary classification problem with noise and outliers. These models in general suffer from the negative effects of having mislabeled training points and disregard position information. In this paper, we propose a novel method to better address these issues. First, we adopt the intuitionistic fuzzy set approach to detect suspectable mislabeled training points. Then we omit their labels but use their full position information to build a semi-supervised support vector machine ((mathrm {S^3VM})) model. After that, we reformulate the corresponding model into a non-convex problem and design a branch-and-bound algorithm to solve it. A new lower bound estimator is used to improve the accuracy and efficiency for binary classification. Numerical tests are conducted to compare the performances of the proposed method with other benchmark support vector machine models. The results strongly support the superior performance of the proposed method. |
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