  Feature selection that selects an informative subset of variables from data
not only enhances the model interpretability and performance but also
alleviates the resource demands. Recently, there has been growing attention on
feature selection using neural networks. However, existing methods usually
suffer from high computational costs when applied to high-dimensional datasets.
In this paper, inspired by evolution processes, we propose a novel
resource-efficient supervised feature selection method using sparse neural
networks, named \enquote{NeuroFS}. By gradually pruning the uninformative
features from the input layer of a sparse neural network trained from scratch,
NeuroFS derives an informative subset of features efficiently. By performing
several experiments on $11$ low and high-dimensional real-world benchmarks of
different types, we demonstrate that NeuroFS achieves the highest ranking-based
score among the considered state-of-the-art supervised feature selection
models. The code is available on GitHub.
