发表机构
Georgia Institute of Technology; University of Connecticut; Columbia University; Texas State University（佐治亚理工学院; 康涅狄格大学; 哥伦比亚大学; 德克萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对深度神经网络在二元分类中可解释性难题，结合懒惰训练思想与变量重要性框架，提出识别重要特征的高效算法，经理论分析、模拟研究和实际应用验证其有效性。
AI 中文摘要
深度神经网络已广泛应用于诸多领域（如计算机视觉和自然语言处理），但其可解释性仍是难题。近期多数研究聚焦回归框架，本文关注二元分类框架，结合懒惰训练思想与变量重要性框架，提出识别重要特征的高效算法。理论上，该方法假设极少且错误率可控。通过大量模拟研究和实际数据应用检验了所提方法及算法的有效性。
英文摘要
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.
