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Group Feature Screening Based on Information Gain Ratio for Ultrahigh‐Dimensional Data

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  • Zhongzheng Wang
  • Guangming Deng
  • Jianqi Yu

Abstract

Most model‐free feature screening approaches focus on the ‐individual predictor; therefore, they are not able to incorporate structured predictors like grouped variables. In this article, we propose a group screening procedure via the information gain ratio for a classification model, which is a direct extension of the original sure independence screening procedure and also model‐free. The proposed method yields a better screening performance and classification accuracy. It is demonstrated that the proposed group screening method possesses the sure screening property and ranking consistency properties under certain regularity conditions. Through simulation studies and real‐world data analysis, we demonstrate the proposed method with the finite sample performance.

Suggested Citation

  • Zhongzheng Wang & Guangming Deng & Jianqi Yu, 2022. "Group Feature Screening Based on Information Gain Ratio for Ultrahigh‐Dimensional Data," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:1600986
    DOI: 10.1155/2022/1600986
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    References listed on IDEAS

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    1. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    2. Qing Mai & Hui Zou, 2013. "The Kolmogorov filter for variable screening in high-dimensional binary classification," Biometrika, Biometrika Trust, vol. 100(1), pages 229-234.
    3. Danyang Huang & Runze Li & Hansheng Wang, 2014. "Feature Screening for Ultrahigh Dimensional Categorical Data With Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 237-244, April.
    4. 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.
    5. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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