GradCFA: 一种基于梯度的反事实和特征归因解释算法，用于神经网络的局部解释
GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks
AI总结：
本文提出GradCFA算法，结合反事实解释和特征归因，提升神经网络的可解释性，通过优化可行性、合理性与多样性，改进现有方法的不足，支持多类场景应用。
摘要：
本文提出GradCFA算法，结合反事实解释和特征归因，提升神经网络的可解释性，通过优化可行性、合理性与多样性，改进现有方法的不足，支持多类场景应用。
英文摘要：
Explainable Artificial Intelligence (XAI) is increasingly essential as AI systems are deployed in critical fields such as healthcare and finance, offering transparency into AI-driven decisions. Two major XAI paradigms, counterfactual explanations (CFX) and feature attribution (FA), serve distinct roles in model interpretability. This study introduces GradCFA, a hybrid framework combining CFX and FA to improve interpretability by explicitly optimizing feasibility, plausibility, and diversity - key qualities often unbalanced in existing methods. Unlike most CFX research focused on binary classification, GradCFA extends to multi-class scenarios, supporting a wider range of applications. We evaluate GradCFA's validity, proximity, sparsity, plausibility, and diversity against state-of-the-art methods, including Wachter, DiCE, CARE for CFX, and SHAP for FA. Results show GradCFA effectively generates feasible, plausible, and diverse counterfactuals while offering valuable FA insights. By identifying influential features and validating their impact, GradCFA advances AI interpretability. The code for implementation of this work can be found at: https://github.com/jacob-ws/GradCFs .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
