Adaptation Strategies for Automated Machine Learning on Evolving Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Celik:2021:PAMI,
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author = "Bilge Celik and Joaquin Vanschoren",
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title = "Adaptation Strategies for Automated Machine Learning
on Evolving Data",
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journal = "IEEE Transactions on Pattern Analysis and Machine
Intelligence",
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year = "2021",
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volume = "43",
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number = "9",
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pages = "3067--3078",
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abstract = "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
concept drift on the performance of AutoML methods, and
which adaptation strategies can be employed to make
them more robust to changes in the underlying data. To
that end, we propose 6 concept drift adaptation
strategies and evaluate their effectiveness on a
variety of AutoML approaches for building machine
learning pipelines, including Bayesian optimization,
genetic programming, and random search with automated
stacking. These are evaluated empirically on real-world
and synthetic data streams with different types of
concept drift. Based on this analysis, we propose ways
to develop more sophisticated and robust AutoML
techniques.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/TPAMI.2021.3062900",
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ISSN = "1939-3539",
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month = sep,
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notes = "Also known as \cite{9366792}",
- }
Genetic Programming entries for
Bilge Celik
Joaquin Vanschoren
Citations