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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Acknowledgements
Tian’s research has been supported by National Natural Science Foundation of China Grants #11401485 and #71331004. Deng’s research has been supported by National Natural Science Foundation of China Grant #11501543 and Scientific Research Foundation of UCAS Grants #Y65201VY00 and #Y65302V1G4. Fangs research has been supported by the US Army Research Office Grant #W911NF-15-1-0223. Luo’s research has been supported by National Natural Science Foundation of China Grant #71701035.
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Tian, Y., Deng, Z., Luo, J. et al. An intuitionistic fuzzy set based S\(^3\)VM model for binary classification with mislabeled information. Fuzzy Optim Decis Making 17, 475–494 (2018). https://doi.org/10.1007/s10700-017-9282-z
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DOI: https://doi.org/10.1007/s10700-017-9282-z