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An Ensemble Method for Feature Screening

Author

Listed:
  • Xi Wu

    (National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100021, China)

  • Shifeng Xiong

    (NCMIS, KLSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Weiyan Mu

    (School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

Abstract

It is known that feature selection/screening for high-dimensional nonparametric models is an important but very difficult issue. In this paper, we first point out the limitations of existing screening methods. In particular, model-free sure independence screening methods, which are defined on random predictors, may completely miss some important features in the underlying nonparametric function when the predictors follow certain distributions. To overcome these limitations, we propose an ensemble screening procedure for nonparametric models. It elaborately combines several existing screening methods and outputs a result close to the best one of these methods. Numerical examples indicate that the proposed method is very competitive and has satisfactory performance even when existing methods fail.

Suggested Citation

  • Xi Wu & Shifeng Xiong & Weiyan Mu, 2023. "An Ensemble Method for Feature Screening," Mathematics, MDPI, vol. 11(2), pages 1-14, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:362-:d:1030966
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    References listed on IDEAS

    as
    1. Shifeng Xiong, 2014. "Better subset regression," Biometrika, Biometrika Trust, vol. 101(1), pages 71-84.
    2. Radchenko, Peter & James, Gareth M., 2010. "Variable Selection Using Adaptive Nonlinear Interaction Structures in High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1541-1553.
    3. Hengjian Cui & Runze Li & Wei Zhong, 2015. "Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 630-641, June.
    4. Runze Li & Wei Zhong & Liping Zhu, 2012. "Feature Screening via Distance Correlation Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1129-1139, September.
    5. Yamada, Shu & Lin, Dennis K. J., 1999. "Three-level supersaturated designs," Statistics & Probability Letters, Elsevier, vol. 45(1), pages 31-39, October.
    6. Xiaofeng Shao & Jingsi Zhang, 2014. "Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1302-1318, September.
    7. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    8. Fan, Jianqing & Feng, Yang & Song, Rui, 2011. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 544-557.
    9. Jianqing Fan & Yunbei Ma & Wei Dai, 2014. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1270-1284, September.
    10. Chen Xu & Jiahua Chen, 2014. "The Sparse MLE for Ultrahigh-Dimensional Feature Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1257-1269, September.
    11. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    12. Tjur, Tue, 2009. "Coefficients of Determination in Logistic Regression Models—A New Proposal: The Coefficient of Discrimination," The American Statistician, American Statistical Association, vol. 63(4), pages 366-372.
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