Adaptation Strategies for Automated Machine Learning on Evolving Data. (arXiv:2006.06480v3 [cs.LG] UPDATED)

Automated Machine Learning (AutoML) systems have been shown to efficiently
build good models for new datasets. However, it is often not clear how well
they can adapt when the data evolves over time. The main goal of this study is
to understand the effect of data stream challenges such as concept drift on the
performance of AutoML methods, and which adaptation strategies can be employed
to make them more robust. To that end, we propose 6 concept drift adaptation
strategies …

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