论文标题
分析性深神经网络的一致特征选择
Consistent Feature Selection for Analytic Deep Neural Networks
论文作者
论文摘要
神经网络模型的可解释性和解释性的最重要步骤之一是特征选择，旨在确定相关特征的子集。该领域的理论结果主要集中在问题的预测方面，由于模型的严重非线性和不识别性，几乎没有针对深神经网络的特征选择一致性的工作。缺乏理论基础对深度学习对正确解释起着核心作用的环境的适用性产生了怀疑。 在这项工作中，我们研究了分析深网的功能选择问题。我们证明，对于广泛的网络，包括深馈神经网络，卷积神经网络和主要的残留神经网络的主要子类，作为基本估计器是选择一致的自适应组套管选择程序。这项工作提供了进一步的证据表明，组套索可能对神经网络的特征选择效率低下，并主张在流行的小组套索上使用自适应组套索。
One of the most important steps toward interpretability and explainability of neural network models is feature selection, which aims to identify the subset of relevant features. Theoretical results in the field have mostly focused on the prediction aspect of the problem with virtually no work on feature selection consistency for deep neural networks due to the model's severe nonlinearity and unidentifiability. This lack of theoretical foundation casts doubt on the applicability of deep learning to contexts where correct interpretations of the features play a central role. In this work, we investigate the problem of feature selection for analytic deep networks. We prove that for a wide class of networks, including deep feed-forward neural networks, convolutional neural networks, and a major sub-class of residual neural networks, the Adaptive Group Lasso selection procedure with Group Lasso as the base estimator is selection-consistent. The work provides further evidence that Group Lasso might be inefficient for feature selection with neural networks and advocates the use of Adaptive Group Lasso over the popular Group Lasso.
