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Dimension reduction-based adaptive-to-model semi-supervised classification

Author

Listed:
  • Xuehu Zhu

    (Xi’an Jiaotong University)

  • Rongzhu Zhao

    (Xi’an Jiaotong University)

  • Dan Zeng

    (Xi’an Jiaotong University)

  • Qian Zhao

    (Xi’an Jiaotong University)

  • Jun Zhang

    (Shenzhen University)

Abstract

This paper introduces a novel Dimension Reduction-based Adaptive-to-model Semi-supervised Classification method, specifically designed for scenarios where the number of unlabeled samples significantly exceeds that of labeled samples. Leveraging the strengths of sufficient dimension reduction and non-parametric interpolation, the method significantly amplifies the value derived from unlabeled samples, thus enhancing the precision of the classification model. An iterative version is also presented to extract further insights from the interpolated unlabeled samples. Theoretical analyses and numerical studies demonstrate substantial improvements in classifier accuracy, particularly in the context of model misspecified. The effectiveness of the proposed method in enhancing classification accuracy is further substantiated through two empirical analyses: credit card application evaluations and coronary heart disease diagnostic assessments.

Suggested Citation

  • Xuehu Zhu & Rongzhu Zhao & Dan Zeng & Qian Zhao & Jun Zhang, 2024. "Dimension reduction-based adaptive-to-model semi-supervised classification," Statistical Papers, Springer, vol. 65(7), pages 4631-4675, September.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:7:d:10.1007_s00362-024-01578-6
    DOI: 10.1007/s00362-024-01578-6
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    References listed on IDEAS

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    1. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2014. "Probability-enhanced sufficient dimension reduction for binary classification," Biometrics, The International Biometric Society, vol. 70(3), pages 546-555, September.
    2. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2017. "Principal weighted support vector machines for sufficient dimension reduction in binary classification," Biometrika, Biometrika Trust, vol. 104(1), pages 67-81.
    3. Li, Junlan & Wang, Tao, 2021. "Dimension reduction in binary response regression: A joint modeling approach," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
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