  In this paper, we propose a model-free feature selection method for
ultra-high dimensional data with mass features. This is a two phases procedure
that we propose to use the fused Kolmogorov filter with the random forest based
RFE to remove model limitations and reduce the computational complexity. The
method is fully nonparametric and can work with various types of datasets. It
has several appealing characteristics, i.e., accuracy, model-free, and
computational efficiency, and can be widely used in practical problems, such as
multiclass 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 the
superior performance of the proposed method over other existing methods by
simulations and real data examples.
