Journal
JOURNAL OF MATHEMATICS
Volume 2022, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2022/1600986
Keywords
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Funding
- National Natural Science Foundation of China
- [71963008]
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The proposed group screening procedure based on the information gain ratio for a classification model is shown to have better screening performance and classification accuracy.
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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