用于基于案例推理的偏好型抽象论证（含附录）
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
AI总结：
提出 AA-CBR-P，将用户偏好融入抽象论证与基于案例推理，并在原发性脑肿瘤临床试验数据上验证其优于其他可解释模型。
摘要：
为提升可解释、数据驱动分类模型的有效性与灵活性，本工作将用户定义偏好与抽象论证和基于案例推理（CBR）进行了新颖结合。具体而言，我们提出用于基于案例推理的偏好型抽象论证（称为 AA-CBR-P），允许用户定义多种比较案例的方法，并通过排序指定其对这些比较方法的偏好。我们证明该模型在作出预测时会内在遵循这些偏好，并表明以往用于基于案例推理的抽象论证方法不足以表达对论证组成部分的偏好。随后，我们展示如何将其应用于一个真实医学数据集，该数据集来自一项评估原发性脑肿瘤患者不同评估方法的临床试验。我们通过实验表明，本方法在该数据集上优于其他可解释机器学习模型。
英文摘要：
In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.
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。
