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AIMS Mathematics

2023, Volume 8, Issue 2: 4342-4362. doi: 10.3934/math.2023216
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Research article

Group feature screening based on Gini impurity for ultrahigh-dimensional multi-classification

  • 1.
    College of science, Guilin University of Technology, Guangxi 541000, China
  • 2.
    Applied Statistics Institute, Guilin University of Technology, Guangxi 541000, China
  • 3.
    School of finance, Jiangxi University of Finance and Economics, Jiangxi 330013, China
  • Received: 04 September 2022 Revised: 17 November 2022 Accepted: 22 November 2022 Published: 05 December 2022

  • MSC : 62H30, 62R07

  • Because the majority of model-free feature screening methods concentrate on individual predictors, they are unable to consider structured predictors, such as grouped variables. In this study, we suggest a model-free and direct extension of the original sure independence screening approach for group screening using Gini impurity for a classification model. Compared to current feature screening approaches, the proposed method performs better in terms of screening efficiency and classification accuracy. It was established that the suggested group screening process exhibits sure screening properties and ranking consistency properties under specific regularity conditions. We used simulation studies to illustrate the limited sample performance of the proposed technique and real data analysis.

    Citation: Zhongzheng Wang, Guangming Deng, Haiyun Xu. Group feature screening based on Gini impurity for ultrahigh-dimensional multi-classification[J]. AIMS Mathematics, 2023, 8(2): 4342-4362. doi: 10.3934/math.2023216

    Related Papers:

  • Because the majority of model-free feature screening methods concentrate on individual predictors, they are unable to consider structured predictors, such as grouped variables. In this study, we suggest a model-free and direct extension of the original sure independence screening approach for group screening using Gini impurity for a classification model. Compared to current feature screening approaches, the proposed method performs better in terms of screening efficiency and classification accuracy. It was established that the suggested group screening process exhibits sure screening properties and ranking consistency properties under specific regularity conditions. We used simulation studies to illustrate the limited sample performance of the proposed technique and real data analysis.


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