Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks

Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, D. Mocanu
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引用次数: 4

Abstract

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.
稀疏神经网络中神经元进化的监督特征选择
特征选择从数据中选择一个信息丰富的变量子集,不仅提高了模型的可解释性和性能,而且减轻了资源需求。近年来,利用神经网络进行特征选择的研究越来越受到人们的关注。然而,现有的方法在处理高维数据集时,通常存在计算成本高的问题。在本文中,受进化过程的启发,我们提出了一种新的基于稀疏神经网络的资源高效监督特征选择方法,命名为\enquote{NeuroFS}。通过从从零开始训练的稀疏神经网络的输入层逐渐修剪无信息特征,NeuroFS有效地派生出信息特征子集。通过在不同类型的$11$低维和高维真实世界基准上进行多次实验,我们证明了NeuroFS在考虑的最先进的监督特征选择模型中获得了最高的基于排名的分数。代码可在GitHub上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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