Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data

• URL: http://arxiv.org/abs/2211.14144v1
• Date: Fri, 25 Nov 2022 14:46:36 GMT
• Title: Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data
• Authors: Can Chen, Scott T. Weiss, Yang-Yu Liu
• Abstract summary: We present a deep learning-based method - GRAph Convolutional nEtwork feature Selector (GRACES) - to select important features for HDLSS data. We demonstrate empirical evidence that GRACES outperforms other feature selection methods on both synthetic and real-world datasets.
• Score: 4.266990593059533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. In this paper, we present a deep learning-based method - GRAph Convolutional nEtwork feature Selector (GRACES) - to select important features for HDLSS data. We demonstrate empirical evidence that GRACES outperforms other feature selection methods on both synthetic and real-world datasets.

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