Tabular biomedical data poses challenges in machine learning because it is often high-dimensional and typically low-sample-size (HDLSS). Previous research has attempted to address these challenges via local feature selection, but existing approaches often fail to achieve optimal performance due to their limitation in identifying globally important features and their susceptibility to the co-adaptation problem. In this paper, we propose ProtoGate, a prototype-based neural model for feature selection on HDLSS data. ProtoGate first selects instance-wise features via adaptively balancing global and local feature selection. Furthermore, ProtoGate employs a non-parametric prototype-based prediction mechanism to tackle the co-adaptation problem, ensuring the feature selection results and predictions are consistent with underlying data clusters. We conduct comprehensive experiments to evaluate the performance and interpretability of ProtoGate on synthetic and real-world datasets. The results show that ProtoGate generally outperforms state-of-the-art methods in prediction accuracy by a clear margin while providing high-fidelity feature selection and explainable predictions. Code is available at https://github.com/SilenceX12138/ProtoGate.

翻译：表格生物医学数据因其高维、小样本（HDLSS）的特性给机器学习带来了挑战。先前研究试图通过局部特征选择应对这些挑战，但现有方法由于难以识别全局重要特征且易受共适应问题影响，往往无法达到最优性能。本文提出ProtoGate，一种面向HDLSS数据的基于原型的神经特征选择模型。ProtoGate首先通过自适应平衡全局与局部特征选择来实现实例级特征筛选；进而采用基于原型的非参数预测机制应对共适应问题，确保特征选择结果与预测同潜在数据簇结构保持一致。我们在合成与真实数据集上进行了全面实验，以评估ProtoGate的性能与可解释性。结果表明，ProtoGate在预测精度上显著优于现有先进方法，同时能够提供高保真度的特征选择与可解释的预测结果。代码发布于https://github.com/SilenceX12138/ProtoGate。