LAUSR

Existing model-free ultra-high-dimensional feature screening methods mainly focus on the individual covariate. However, many variables have a community structure, such as grouped covariates in which all variates have high correlation and some associations in one group. There are some research studies about grouped feature screening for ultra-high-dimensional data for a linear model by promoting sure independence screening and high-dimensional ordinary least-squares projector to the group version. Hence, we propose a new ultra-high-dimensional grouped feature screening method for a classification model, which is model-free and suitable for continuous and categorical covariates. Compared with individual covariate feature screening, the proposed method yields a better screening performance and classification accuracy. The grouped feature screening and ranking consistency properties of the proposed method are established. We illustrate the finite sample performance of the proposed method by simulation and real data analysis.