Bakirov, R., Fay, D. and Gabrys, B., 2021. Automated adaptation strategies for stream learning. Machine Learning, 110, 1429-1462.
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DOI: 10.1007/s10994-021-05992-x
Abstract
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.
Item Type: | Article |
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ISSN: | 0885-6125 |
Uncontrolled Keywords: | Adaptive machine learning · Streaming data · Non-stationary data · Concept drift · Automated machine learning |
Group: | Faculty of Science & Technology |
ID Code: | 35640 |
Deposited By: | Symplectic RT2 |
Deposited On: | 14 Jun 2021 14:29 |
Last Modified: | 14 Mar 2022 14:28 |
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