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Nandy, D., Chiaromonte, F. and Li, R.Z. (2021) Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems. Journal of the American Statistical Association, 117, 1516-1529.
https://doi.org/10.1080/01621459.2020.1864380

has been cited by the following article:

• TITLE: Model-Free Feature Screening Based on Gini Impurity for Ultrahigh-Dimensional Multiclass Classification

  AUTHORS: Zhongzheng Wang, Guangming Deng

  KEYWORDS: Ultrahigh-Dimensional, Feature Screening, Model-Free, Gini Impurity, Multiclass Classification

  JOURNAL NAME: Open Journal of Statistics, Vol.12 No.5, October 27, 2022

  ABSTRACT: It is quite common that both categorical and continuous covariates appear in the data. But, most feature screening methods for ultrahigh-dimensional classification assume the covariates are continuous. And applicable feature screening method is very limited; to handle this non-trivial situation, we propose a model-free feature screening for ultrahigh-dimensional multi-classification with both categorical and continuous covariates. The proposed feature screening method will be based on Gini impurity to evaluate the prediction power of covariates. Under certain regularity conditions, it is proved that the proposed screening procedure possesses the sure screening property and ranking consistency properties. We demonstrate the finite sample performance of the proposed procedure by simulation studies and illustrate using real data analysis.