一句话：提出 AutoConf，用蜕变测试与贝叶斯优化自动配置聚类无监督学习系统，提升异常检测效果。
无监督学习系统在无标签数据中发现模式与结构方面应用广泛，但其效果高度依赖配置。选择合适的聚类算法与超参数通常需要领域知识，并伴随大量人工试错。本文提出、应用并评估一种自动化配置方法 AutoConf，面向基于聚类的无监督学习系统。该方法利用蜕变测试，通过施加一系列输入变换来验证配置，并以蜕变测试的输出指导贝叶斯优化，从而自动搜索最优配置。作者在来自三个领域的六个异常检测数据集上进行了实验。结果表明，AutoConf 能找到优于基线方法的配置，平均召回率达到 0.89，精确率达到 0.84。
Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the complexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for configuring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a series of input transformations. We use Bayesian optimization guided by metamorphic-testing output to automatically identify the optimal configuration. The approach aims to streamline the configuration process and enhance the effectiveness of unsupervised learning systems. It has been evaluated through experiments on six datasets from three domains for anomaly detection. The evaluation results show that our approach can find configurations outperforming the baseline approaches as they achieved a recall of 0.89 and a precision of 0.84 (on average).
@inproceedings{SharGHSTK23,
title = {AutoConf: Automated Configuration of Unsupervised Learning Systems Using Metamorphic Testing and Bayesian Optimization},
author = {Lwin Khin Shar and Arda Goknil and Erik Johannes Husom and Sagar Sen and Yan Naing Tun and Kisub Kim},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00094}
}