Concept Drift Adaptation
Concept Drift Adaptation is a computational learning strategy that enables a model to dynamically adjust its parameters or structure in response to non-stationary data distributions, thereby maintaining predictive accuracy as underlying patterns evolve over time.
285
Publications
16.1K
Citations
830
Authors
374
Institutions
Publications per year
2017–2026
184
Parents
285
Learning in the Presence of Concept Drift and Hidden Contexts
Gerhard Widmer, Miroslav Kubát · Machine Learning · 1996 · 1.4K citations · Full text
Learning in the presence of concept drift and hidden contexts
Gerhard Widmer, Miroslav Kubát · Machine Learning · 1996 · 993 citations · Full text
Learning under Concept Drift: A Review
IEEE Transactions on Knowledge and Data Engineering · 2018 · 873 citations · Full text
New ensemble methods for evolving data streams
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer et al. · 2009 · 564 citations · Full text
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374
University of Technology Sydney
Sydney, Australia
14 h-index
64 publications
2.7K citations

University of Waikato
Hamilton, New Zealand
10 h-index
20 publications
3K citations

Wrocław University of Science and Technology
Wroclaw, Poland
7 h-index
11 publications
462 citations
Virginia Commonwealth University
Richmond, United States
6 h-index
11 publications
306 citations
Politecnico di Milano
Milan, Italy
6 h-index
11 publications
475 citations

South China University of Technology
Guangzhou, China
5 h-index
10 publications
311 citations
Universidade de São Paulo
São Paulo, Brazil
5 h-index
7 publications
558 citations
TU Dortmund University
Dortmund, Germany
5 h-index
7 publications
976 citations
1–8 of 374
13 publications · 1.1K citations
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11 publications · 898 citations
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10 publications · 520 citations
10
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Repository · 9 publications · 650 citations
9
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TKDE · 8 publications · 378 citations
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