Concept Drift
Concept Drift is a phenomenon in information filtering whereby the statistical properties of the target variable or underlying data distribution change over time in an unforeseen manner, thereby compromising the performance of learning models trained on historical data.
904
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2017–2026
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Learning from Time-Changing Data with Adaptive Windowing
Albert Bifet, Ricard Gavaldà · 2007 · 1.7K citations
Learning in the Presence of Concept Drift and Hidden Contexts
Gerhard Widmer, Miroslav Kubát · Machine Learning · 1996 · 1.4K citations · Full text
Mining concept-drifting data streams using ensemble classifiers
Haixun Wang, Wei Fan, Philip S. Yu et al. · 2003 · 1.3K citations
Collaborative filtering with temporal dynamics
Yehuda Koren · 2009 · 1.2K citations
Learning in the presence of concept drift and hidden contexts
Gerhard Widmer, Miroslav Kubát · Machine Learning · 1996 · 993 citations · Full text
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University of Technology Sydney
Sydney, Australia
26 h-index
116 publications
6.4K citations
Politecnico di Milano
Milan, Italy
16 h-index
38 publications
3K citations

Wrocław University of Science and Technology
Wroclaw, Poland
14 h-index
28 publications
900 citations

University of Waikato
Hamilton, New Zealand
14 h-index
32 publications
3.6K citations

Universidade Federal de Pernambuco
Recife, Brazil
14 h-index
37 publications
2.3K citations
University of Illinois Urbana-Champaign
Urbana, United States
14 h-index
28 publications
4.8K citations
Universidade de São Paulo
São Paulo, Brazil
12 h-index
31 publications
1.5K citations
Eindhoven University of Technology
Eindhoven, The Netherlands
12 h-index
22 publications
1.4K citations
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27 publications · 3.5K citations
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26 publications · 1.9K citations
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TKDE · 25 publications · 3.3K citations
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27 publications · 1.3K citations
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