Features Screening for Ultra-High Dimensional Discriminant Data Based on Gini Correlation Coefficient
This paper proposes a model-free discriminant screening method based on the Gini correlation coefficient for screening continuous features in ultra-high dimensional classification data.Additionally,the method can be generalized to cases where the response variable is continuous and the independent variable is discrete.The proposed feature screening method satisfies the sure screening property and ranking consistency property under certain regular conditions.Finally,the effectiveness of the screening method has been verified through Monte Carlo simulation and analysis of real data.This study provides a novel approach to feature selection in high-dimensional data and extends the application of the concept of independence in statistical theory.
万方数据
维普
