  Feature selection is important step in machine learning since it has shown to
improve prediction accuracy while depressing the curse of dimensionality of
high dimensional data. The neural networks have experienced tremendous success
in solving many nonlinear learning problems. Here, we propose new
neural-network based feature selection approach that introduces two constrains,
the satisfying of which leads to sparse FS layer. We have performed extensive
experiments on synthetic and real world data to evaluate performance of the
proposed FS. In experiments we focus on the high dimension, low sample size
data since those represent the main challenge for feature selection. The
results confirm that proposed Feature Selection Based on Sparse Neural Network
Layer with Normalizing Constraints (SNEL-FS) is able to select the important
features and yields superior performance compared to other conventional FS
methods.
