论文标题
用于高维和低样本尺寸数据的基于图形卷积网络的特征选择
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data
论文作者
论文摘要
功能选择是一种强大的尺寸缩小技术，它为模型构建选择了相关特征的子集。已经提出了许多特征选择方法，但是由于过度拟合的挑战，其中大多数在高维和低样本尺寸（HDLS）设置下失败。在本文中，我们提出了一种基于深度学习的方法 - 图形卷积网络特征选择器（Graces） - 为HDLSS数据选择重要功能。我们证明了经验证据，这些证据在合成和现实世界数据集上都优于其他特征选择方法。
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.
