鲁棒LassoNet：通过鲁棒损失函数增强神经网络中的特征选择
Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
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中文总结 AI 辅助
针对神经网络特征选择对异常值敏感的问题，提出鲁棒LassoNet，通过引入Huber、Cauchy等鲁棒损失函数，在保持优化框架的同时提升污染数据下的预测与特征选择性能。
中文摘要 AI 辅助
神经网络中的特征选择仍然是一个具有挑战性的问题，尤其是在存在噪声或污染数据的情况下。LassoNet是一种近期提出的方法，通过将神经网络与层次稀疏约束相结合来解决这一问题，从而实现预测和变量选择的同步进行。然而，其标准公式依赖于均方误差（MSE）损失，而众所周知，该损失对异常值高度敏感。在本文中，我们提出了鲁棒LassoNet，它是LassoNet的扩展，引入了鲁棒损失函数，如Huber、Cauchy、Tukey's bisquare和Nonnegative Garrote，以减轻极端观测的影响。所提出的方法保留了原始的优化框架，同时提高了在数据污染下的稳定性。通过在合成数据集和真实数据集上的实验，我们表明，在存在异常值或重尾噪声的情况下，鲁棒LassoNet显著提高了预测性能和特征选择准确性，同时在干净设置下保持了相当的性能。这些结果凸显了鲁棒性在基于神经网络的特征选择中的重要性，并为选择适当的损失函数提供了实用指南。
英文摘要
Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent approach that addresses this issue by combining neural networks with hierarchical sparsity constraints, enabling simultaneous prediction and variable selection. However, its standard formulation relies on the mean squared error (MSE) loss, which is known to be highly sensitive to outliers. In this paper, we present Robust LassoNet, an extension of LassoNet that incorporates robust loss functions, such as Huber, Cauchy, Tukey's bisquare, and Nonnegative Garrote, to mitigate the effect of extreme observations. The proposed approach preserves the original optimization framework while improving stability under data contamination. Through experiments on synthetic and real datasets, we show that robust LassoNet significantly improves both predictive performance and feature selection accuracy in the presence of outliers or heavy tailed noise, while maintaining comparable performance in clean settings. These results highlight the importance of robustness in neural network based feature selection and suggest practical guidelines for choosing appropriate loss functions.
