  The presence of irrelevant features in the input dataset tends to reduce the
interpretability and predictive quality of machine learning models. Therefore,
the development of feature selection methods to recognize irrelevant features
is a crucial topic in machine learning. Here we show how the General Regression
Neural Network used with an anisotropic Gaussian Kernel can be used to perform
feature selection. A number of numerical experiments are conducted using
simulated data to study the robustness of the proposed methodology and its
sensitivity to sample size. Finally, a comparison with four other feature
selection methods is performed on several real world datasets.
