TY  - JOUR
AU  - Wang, Zhongzheng
AU  - Deng, Guangming
AU  - Xu, Haiyun
PY  - 2023
TI  - Group feature screening based on Gini impurity for ultrahigh-dimensional multi-classification
JO  - AIMS Mathematics
SP  - 4342
EP  - 4362
VL  - 8
IS  - 2
AB  - 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.
UR  - https://doi.org/10.3934/math.2023216
DO  - 10.3934/math.2023216