提出新方法快速识别二分类中关键特征，理论可靠且高效。
Variable Importance Identification Through Lazy Training for Binary Classification
- 结合懒惰训练与变量重要性框架，设计高效算法
- 理论基础弱，误差控制良好，适用于二分类场景
- 适合需要解释性的机器学习应用，如医疗诊断
深度神经网络在计算机视觉、自然语言处理等众多领域广泛应用，但其可解释性仍是难题。现有研究多集中于回归任务，本文转向二分类框架，结合变量重要性与懒惰训练思想，提出一种高效的重要特征识别算法。该方法仅需极少假设，具备良好的误差控制能力。通过大量模拟实验与真实数据应用验证了方法的有效性与实用性。
原文摘要 · Abstract (English)
Deep neural networks have been widely used in many applications (e.g., computer vision and natural language processing); however, understanding their explainability remains a challenging task. Recently, substantial research has been devoted to improving the explainability of deep neural networks, with most of this work focusing on the regression framework. In this paper, we instead focus on the binary classification framework and adopt a variable-importance framework combined with the idea of lazy training to propose an efficient algorithm for identifying important features. From a theoretical perspective, our method relies on only a minimal set of assumptions and achieves well-controlled error rates. The validity of the proposed method and algorithm is examined through extensive simulation studies and real-data applications.
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