Concept Drift Modeling
Concept Drift Modeling is a computational framework within knowledge modeling that formally represents and detects temporal changes in the underlying data distribution or concept relationships over time, distinguishing it from static modeling approaches by its focus on dynamic concept evolution.
420
Publications
31.9K
Citations
1.2K
Authors
490
Institutions
Publications per year
2017–2026
249
Parents
420
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
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
Unsupervised real-time anomaly detection for streaming data
Subutai Ahmad, Alexander Lavin, Scott Purdy et al. · Neurocomputing · 2017 · 969 citations · Full text
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University of Technology Sydney
Sydney, Australia
14 h-index
59 publications
3.4K citations
Eindhoven University of Technology
Eindhoven, The Netherlands
11 h-index
20 publications
1.4K citations

Universidade Federal de Pernambuco
Recife, Brazil
10 h-index
24 publications
1.7K citations

University of Waikato
Hamilton, New Zealand
9 h-index
15 publications
2.9K citations
Politecnico di Milano
Milan, Italy
7 h-index
16 publications
1.9K citations
Bielefeld University
Bielefeld, Germany
7 h-index
17 publications
969 citations

Wrocław University of Science and Technology
Wroclaw, Poland
6 h-index
9 publications
320 citations

Hefei University of Technology
Hefei, China
5 h-index
20 publications
988 citations
1–8 of 490
20 publications · 1.4K citations
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15 publications · 1.9K citations
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15 publications · 757 citations
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Repository · 12 publications · 783 citations
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12 publications · 836 citations
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