  The application of machine learning to image and video data often yields a
high dimensional feature space. Effective feature selection techniques identify
a discriminant feature subspace that lowers computational and modeling costs
with little performance degradation. A novel supervised feature selection
methodology is proposed for machine learning decisions in this work. The
resulting tests are called the discriminant feature test (DFT) and the relevant
feature test (RFT) for the classification and regression problems,
respectively. The DFT and RFT procedures are described in detail. Furthermore,
we compare the effectiveness of DFT and RFT with several classic feature
selection methods. To this end, we use deep features obtained by LeNet-5 for
MNIST and Fashion-MNIST datasets as illustrative examples. Other datasets with
handcrafted and gene expressions features are also included for performance
evaluation. It is shown by experimental results that DFT and RFT can select a
lower dimensional feature subspace distinctly and robustly while maintaining
high decision performance.
