  Random forests are a widely used machine learning algorithm, but their
computational efficiency is undermined when applied to large-scale datasets
with numerous instances and useless features. Herein, we propose a
nonparametric feature selection algorithm that incorporates random forests and
deep neural networks, and its theoretical properties are also investigated
under regularity conditions. Using different synthetic models and a real-world
example, we demonstrate the advantage of the proposed algorithm over other
alternatives in terms of identifying useful features, avoiding useless ones,
and the computation efficiency. Although the algorithm is proposed using
standard random forests, it can be widely adapted to other machine learning
algorithms, as long as features can be sorted accordingly.
