  Excluding irrelevant features in a pattern recognition task plays an
important role in maintaining a simpler machine learning model and optimizing
the computational efficiency. Nowadays with the rise of large scale datasets,
feature selection is in great demand as it becomes a central issue when facing
high-dimensional datasets. The present study provides a new measure of saliency
for features by employing a Sensitivity Analysis (SA) technique called the
extended Fourier amplitude sensitivity test, and a well-trained Feedforward
Neural Network (FNN) model, which ultimately leads to the selection of a
promising optimal feature subset. Ideas of the paper are mainly demonstrated
based on adopting FNN model for feature selection in classification problems.
But in the end, a generalization framework is discussed in order to give
insights into the usage in regression problems as well as expressing how other
function approximate models can be deployed. Effectiveness of the proposed
method is verified by result analysis and data visualization for a series of
experiments over several well-known datasets drawn from UCI machine learning
repository.
