  The problem of selecting a handful of truly relevant variables in supervised
machine learning algorithms is a challenging problem in terms of untestable
assumptions that must hold and unavailability of theoretical assurances that
selection errors are under control. We propose a distribution-free feature
selection method, referred to as Data Splitting Selection (DSS) which controls
False Discovery Rate (FDR) of feature selection while obtaining a high power.
Another version of DSS is proposed with a higher power which "almost" controls
FDR. No assumptions are made on the distribution of the response or on the
joint distribution of the features. Extensive simulation is performed to
compare the performance of the proposed methods with the existing ones.
