Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.

机器学习模型开发的自动化正日益成为一个既定的研究领域。虽然已经深入研究了自动模型选择和自动数据预处理，但是当有多种策略可用时，在自动模型适应策略方面存在差距。手动制定适应策略既费时又费钱。在本文中，我们通过提出使用灵活的自适应机制部署来自动开发自适应策略来解决这个问题。在 36 个数据集上使用所提出的策略和五种自适应算法后的实验结果证实了它们的可行性。这些策略与自定义适应策略和任何单个自适应机制的重复部署相比，实现了更好或可比的性能。