Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data
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 dir...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/1600986 |
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| Summary: | 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. |
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| ISSN: | 2314-4785 |