论文标题
测试汽车系统的鲁棒性
Testing the Robustness of AutoML Systems
论文作者
论文摘要
自动化机器学习（AUTOML）系统旨在找到自动与手头的任务和数据匹配的最佳机器学习（ML）管道。我们研究了使用三个汽车系统（TPOT，H2O和Autokeras）生成的机器学习管道的鲁棒性。特别是，我们研究了肮脏数据对准确性的影响，并考虑使用肮脏的训练数据如何有助于创建更强大的解决方案。此外，我们还分析了生成管道的结构在不同情况下如何不同。
Automated machine learning (AutoML) systems aim at finding the best machine learning (ML) pipeline that automatically matches the task and data at hand. We investigate the robustness of machine learning pipelines generated with three AutoML systems, TPOT, H2O, and AutoKeras. In particular, we study the influence of dirty data on accuracy, and consider how using dirty training data may help create more robust solutions. Furthermore, we also analyze how the structure of the generated pipelines differs in different cases.
