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In this paper, we propose a model-free feature selection method forultra-high dimensional data with mass features. This is a two phases procedurethat we propose to use the fused Kolmogorov filter with the random forest basedRFE to remove model limitations and reduce the computational complexity. Themethod is fully nonparametric and can work with various types of datasets. Ithas several appealing characteristics, i.e., accuracy, model-free, andcomputational efficiency, and can be widely used in practical problems, such asmulticlass classification, nonparametric regression, and Poisson regression,among others. We show that the proposed method is selection consistent and$L_2$ consistent under weak regularity conditions. We further demonstrate thesuperior performance of the proposed method over other existing methods bysimulations and real data examples.

A model-free feature selection technique of feature screening and random forest based recursive feature elimination