Web: http://arxiv.org/abs/2006.06480

May 11, 2022, 1:11 a.m. | Bilge Celik, Joaquin Vanschoren

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv automated machine learning data learning machine machine learning on strategies

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