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Entropy-based model-free feature screening for ultrahigh-dimensional multiclass classification

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  • Lyu Ni
  • Fang Fang

Abstract

Most feature screening methods for ultrahigh-dimensional classification explicitly or implicitly assume the covariates are continuous. However, in the practice, it is quite common that both categorical and continuous covariates appear in the data, and applicable feature screening method is very limited. To handle this non-trivial situation, we propose an entropy-based feature screening method, which is model free and provides a unified screening procedure for both categorical and continuous covariates. We establish the sure screening and ranking consistency properties of the proposed procedure. We investigate the finite sample performance of the proposed procedure by simulation studies and illustrate the method by a real data analysis.

Suggested Citation

  • Lyu Ni & Fang Fang, 2016. "Entropy-based model-free feature screening for ultrahigh-dimensional multiclass classification," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(3), pages 515-530, September.
    • Handle: RePEc:taf:gnstxx:v:28:y:2016:i:3:p:515-530
    DOI: 10.1080/10485252.2016.1167206
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    Citations

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    Cited by:

    1. Zhao, Shaofei & Fu, Guifang, 2022. "Distribution-free and model-free multivariate feature screening via multivariate rank distance correlation," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    2. Xiong, Wei & Chen, Yaxian & Ma, Shuangge, 2023. "Unified model-free interaction screening via CV-entropy filter," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    3. Tao Huang & Jialiang Li, 2018. "Semiparametric model average prediction in panel data analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 125-144, January.
    4. Manhao Luo & Shuangyun Peng & Yanbo Cao & Jing Liu & Bangmei Huang, 2023. "Earthquake fatality prediction based on hybrid feature importance assessment: a case study in Yunnan Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3353-3376, April.
    5. Xianwen Ding & Jiandong Chen & Xueping Chen, 2020. "Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(5), pages 545-568, July.
    6. Lyu Ni & Fang Fang & Fangjiao Wan, 2017. "Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 805-828, November.
    7. Randall Reese & Guifang Fu & Geran Zhao & Xiaotian Dai & Xiaotian Li & Kenneth Chiu, 2022. "Epistasis Detection via the Joint Cumulant," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 514-532, December.

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