结合领域知识和可行性约束的可操作且多样化的反事实解释
Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
AI总结：
提出DANCE方法，通过建模特征依赖和领域约束生成可操作、多样化的反事实解释，在OpenML数据集和工业邮件营销场景中验证了其有效性和实用性。
摘要：
反事实解释通过识别实现期望结果所需的最小变化来提高机器学习模型的可操作可解释性。然而，现有方法常常忽略特征之间的依赖关系，这可能导致不现实或不切实际的修改。这一限制降低了反事实解释在现实决策支持系统中的实用性。受网络安全中电子邮件营销应用的启发，我们提出了DANCE（多样化、可操作且知识约束的解释），一种生成反事实的方法，该方法结合了特征依赖和领域约束。DANCE使用线性或概率结构对特征之间的关系进行建模，这些结构可以从数据中学习或由专家指定。在搜索过程中强制执行这些依赖关系以提高可行性和现实性。该方法在一个统一的目标中联合优化可行性、多样性、邻近性和稀疏性。我们在OpenML的140个数据集上评估了DANCE，并证明它在多个评估标准上相比现有方法具有竞争性或更优的性能。此外，我们与一个电子邮件营销平台合作，在真实工业环境中验证了该方法，表明它能够产生符合领域且可操作的建议。
英文摘要：
Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a desired outcome. However, existing methods often neglect dependencies among features, which can lead to unrealistic or impractical modifications. This limitation reduces the usefulness of counterfactual explanations in real-world decision-support systems. Motivated by applications in cybersecurity for email marketing, we propose DANCE (Diverse, Actionable, and Knowledge-Constrained Explanations), a method for generating counterfactuals that incorporate feature dependencies and domain constraints. DANCE models relationships between features using linear and probabilistic structures that can be learned from data or specified by experts. These dependencies are enforced during the search process to improve plausibility and feasibility. The method jointly optimizes plausibility, diversity, proximity, and sparsity within a unified objective. We evaluate DANCE on 140 datasets from OpenML and demonstrate that it achieves competitive or superior performance compared to existing approaches across multiple evaluation criteria. Additionally, we validate the method in a real-world industrial setting in collaboration with an email marketing platform, showing that it produces domain-consistent and actionable recommendations.
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。
