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An intuitionistic fuzzy set based S $$^3$$ 3 VM model for binary classification with mislabeled information

Author

Listed:
  • Ye Tian

    (Southwestern University of Finance and Economics)

  • Zhibin Deng

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Jian Luo

    (Dongbei University of Finance and Economics)

  • Yueqing Li

    (Lamar University)

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 ( $$\mathrm {S^3VM}$$ S 3 VM ) 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.

Suggested Citation

  • Ye Tian & Zhibin Deng & Jian Luo & Yueqing Li, 2018. "An intuitionistic fuzzy set based S $$^3$$ 3 VM model for binary classification with mislabeled information," Fuzzy Optimization and Decision Making, Springer, vol. 17(4), pages 475-494, December.
    • Handle: RePEc:spr:fuzodm:v:17:y:2018:i:4:d:10.1007_s10700-017-9282-z
    DOI: 10.1007/s10700-017-9282-z
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    References listed on IDEAS

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    1. Huang, Xiaolin & Shi, Lei & Suykens, Johan A.K., 2014. "Asymmetric least squares support vector machine classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 395-405.
    2. Xunjie Gou & Zeshui Xu, 2017. "Exponential operations for intuitionistic fuzzy numbers and interval numbers in multi-attribute decision making," Fuzzy Optimization and Decision Making, Springer, vol. 16(2), pages 183-204, June.
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    Cited by:

    1. Tianna Zhao & Yuanjian Zhang & Duoqian Miao, 2022. "Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification," Mathematics, MDPI, vol. 10(11), pages 1-21, May.

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