论文标题
通过自适应组拉索为神经网络的一致特征选择
Consistent feature selection for neural networks via Adaptive Group Lasso
论文作者
论文摘要
大量在医学和工程科学中广泛使用深度学习的主要障碍是其解释性。尽管神经网络模型是进行预测的强大工具，但它们通常很少提供有关哪些功能在影响预测准确性方面起着重要作用的信息。为了克服这个问题，已经提出了许多通过神经网络进行学习的正规化程序，以降低非重要功能。不幸的是，缺乏理论结果对此类管道的适用性产生了怀疑。在这项工作中，我们建议并建立理论保证，用于使用自适应组套索来选择神经网络的重要特征。具体而言，我们表明我们的特征选择方法对于具有一个隐藏层和双曲线切线激活函数的单输出进发神经网络是一致的。我们使用仿真和数据分析证明其适用性。
One main obstacle for the wide use of deep learning in medical and engineering sciences is its interpretability. While neural network models are strong tools for making predictions, they often provide little information about which features play significant roles in influencing the prediction accuracy. To overcome this issue, many regularization procedures for learning with neural networks have been proposed for dropping non-significant features. Unfortunately, the lack of theoretical results casts doubt on the applicability of such pipelines. In this work, we propose and establish a theoretical guarantee for the use of the adaptive group lasso for selecting important features of neural networks. Specifically, we show that our feature selection method is consistent for single-output feed-forward neural networks with one hidden layer and hyperbolic tangent activation function. We demonstrate its applicability using both simulation and data analysis.
