支持度更高的解释：q-一致汇总的加权列抽样优化
Explaining with Greater Support: Weighted Column Sampling Optimization for q-Consistent Summary-Explanations
摘要：
机器学习系统已被广泛用作需要关键决策的领域的辅助工具，例如医疗保健和刑事司法。决策的可解释性对于用户建立对这些系统的信任至关重要。近年来，基于规则的全局一致性摘要解释及其最大支持度问题（MS）被提出，它可以为特定的决策提供解释以及数据集的有用统计信息。然而，具有有限复杂性的全局一致的摘要解释通常具有小的支持（如果有的话）。在本文中，我们提出了一个宽松的版本的摘要解释，即，$q$ -一致的摘要解释，其目的是以稍低的一致性为代价来获得更大的支持。挑战在于，$q$ -一致性摘要解释（MSqC）的最大支持问题比原始MS问题复杂得多，导致使用标准分支定界求解器的求解时间过长。为了提高求解时间效率，提出了加权列抽样（WCS）方法，该方法通过对变量按其简化的增加支持度（SIS）值进行抽样来求解较小的问题。实验结果表明，基于SIS的WCS方法不仅具有更高的可扩展性，而且能够获得更大的支持度和更好的全局外推效果。
英文摘要：
Machine learning systems have been extensively used as auxiliary tools in domains that require critical decision-making, such as healthcare and criminal justice. The explainability of decisions is crucial for users to develop trust on these systems. In recent years, the globally-consistent rule-based summary-explanation and its max-support (MS) problem have been proposed, which can provide explanations for particular decisions along with useful statistics of the dataset. However, globally-consistent summary-explanations with limited complexity typically have small supports, if there are any. In this paper, we propose a relaxed version of summary-explanation, i.e., the $q$-consistent summary-explanation, which aims to achieve greater support at the cost of slightly lower consistency. The challenge is that the max-support problem of $q$-consistent summary-explanation (MSqC) is much more complex than the original MS problem, resulting in over-extended solution time using standard branch-and-bound solvers. To improve the solution time efficiency, this paper proposes the weighted column sampling~(WCS) method based on solving smaller problems by sampling variables according to their simplified increase support (SIS) values. Experiments verify that solving MSqC with the proposed SIS-based WCS method is not only more scalable in efficiency, but also yields solutions with greater support and better global extrapolation effectiveness.
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。
