Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

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 in 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 "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. We will make the code publicly available on GitHub after acceptance of the paper.

Authors

• Zahra Atashgahi
• Xuhao Zhang
• Neil Kichler
• Shiwei Liu University of Illinois at Urbana-Champaign
• Lu Yin Eindhoven University of Technology
• Mykola Pechenizkiy Eindhoven University of Technology
• Raymond Veldhuis
• Decebal Constantin Mocanu

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