基于列生成的一对多反事实解释
One-for-many Counterfactual Explanations by Column Generation
AI总结：
本文针对一组实例的一对多反事实解释生成问题，首次提出考虑稀疏性的最小化解释数量方案，构建适用于任意黑盒分类器的列生成框架，在可扩展性、计算性能和解质量上均优于现有混合整数规划改编方法。
摘要：
本文研究采用一对多分配规则为一组实例生成反事实解释集的问题，即一条解释可分配给一个实例子群。我们首次解决了在通过限制每条解释中允许共同更改的特征数量来考虑稀疏性的前提下，最小化解释所有实例所需解释数量的问题。本文提出了一种新颖的列生成框架，可高效搜索解释，该框架适用于任意黑盒分类器，如神经网络。与对现有文献中混合整数规划公式的简单改编方法相比，该列生成框架在可扩展性、计算性能和解的质量方面均更具优势。
英文摘要：
In this paper, we consider the problem of generating a set of counterfactual explanations for a group of instances, with the one-for-many allocation rule, where one explanation is allocated to a subgroup of the instances. For the first time, we solve the problem of minimizing the number of explanations needed to explain all the instances, while considering sparsity by limiting the number of features allowed to be changed collectively in each explanation. A novel column generation framework is developed to efficiently search for the explanations. Our framework can be applied to any black-box classifier, like neural networks. Compared with a simple adaptation of a mixed-integer programming formulation from the literature, the column generation framework dominates in terms of scalability, computational performance and quality of the solutions.
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。
