论文标题
Kolmogorov-Smirnov测试中可理解的反事实解释
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test
论文作者
论文摘要
Kolmogorov-Smirnov（KS）测试通常用于许多应用中，例如异常检测，天文学，数据库安全性和AI系统。一项挑战仍然没有受到影响，我们如何获得有关测试集为何失败KS测试的解释。在本文中，我们解决了产生反事实解释的问题，以使KS测试失败。从概念上讲，我们提出了最可理解的反事实解释的概念，该解释同时适合KS测试数据和用户域知识，以产生解释。在计算方面，我们开发了一种有效的算法摩切尔（对于最可理解的说明），该货币避免了枚举和检查测试集的指数级数，使KS测试失败。摩切族不仅保证产生最可理解的反事实解释，而且比基线更快的幅度。在实验方面，我们对一系列基准实际数据集进行了系统的经验研究，以验证最可理解的反事实解释和摩切尔的有效性，效率和可扩展性。
The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why a test set fails the KS test. In this paper, we tackle the problem of producing counterfactual explanations for test data failing the KS test. Concept-wise, we propose the notion of most comprehensible counterfactual explanations, which accommodates both the KS test data and the user domain knowledge in producing explanations. Computation-wise, we develop an efficient algorithm MOCHE (for MOst CompreHensible Explanation) that avoids enumerating and checking an exponential number of subsets of the test set failing the KS test. MOCHE not only guarantees to produce the most comprehensible counterfactual explanations, but also is orders of magnitudes faster than the baselines. Experiment-wise, we present a systematic empirical study on a series of benchmark real datasets to verify the effectiveness, efficiency and scalability of most comprehensible counterfactual explanations and MOCHE.
