Generic adaptation strategies for automated machine learning

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, including estimation of relevant parameters can be time consuming and costly. In this paper we address this issue by proposing generic adaptation strategies based on approaches from earlier works. Experimental results after using the proposed strategies with three adaptive algorithms on 36 datasets confirm their viability. These strategies often achieve better or comparable performance with custom adaptation strategies and naive methods such as repeatedly using only one adaptive mechanism.