Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data
Machine Learning
2023-07-07 v1
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.
Cite
@article{arxiv.2211.14144,
title = {Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data},
author = {Can Chen and Scott T. Weiss and Yang-Yu Liu},
journal= {arXiv preprint arXiv:2211.14144},
year = {2023}
}
Comments
24 pages, 4 figures, 4 tables