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A survey on concept drift adaptation
Open accessAbstract
Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
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“particularly as the phenomena the AI system is modelling are likely to change over time, and the risks and impacts may change with them.”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Gama et al 2014
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“It might be that some relevant records are not presented to the reviewer because the text used in the abstract is not seen as potentially relevant because of concept ambiguity [11, 17], which can make finding relevant records challenging.”
From The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses (2024) · cites this paper as [17]
Experimental — accuracy not yet measuredIn the citing paper
“Records can be hard to find due to concept ambiguity, the different angles from which a subject can be studied, and changes in the meaning of a concept over time, known as concept drift (Chen et al., 2012; Gama et al., 2014).”
From Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders (2023) · cites this paper as Gama et al., 2014
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“Lwakatare et al. [15] and Kim et al. [40] reported the difficulty of specifying desirable datasets.”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [15]
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“Developing the skill set for ML applications is the major challenge related to this KA [15,16,27,32,39,40,59].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [15]
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Metadata
- Authors
- Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A.
- Affiliations
- Universidade do Porto (PT)
- Helsinki Institute for Information Technology (FI)
- Yahoo (Spain) (ES)
- Eindhoven University of Technology (NL)
- Bournemouth University (GB)
- Venue
- ACM Computing Surveys
- Published
- 2014-03
- Volume
- 46
- Issue
- 4
- Pages
- 1-37
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2099419573
- Cited by
- 3,735
- References
- 171
- Citations by year
- 2026: 688 · 2025: 435 · 2024: 296 · 2023: 345 · 2022: 306 · 2021: 367 · 2020: 327 · 2019: 281 · 2018: 229 · 2017: 197 · 2016: 137 · 2015: 96 · 2014: 27
- FWCI
- 151.70
- Type
- journal article
- Language
- en
- Open access
- Locations
- ACM Computing Surveys (publishedVersion)
- TU/e Research Portal (publishedVersion)
- Bournemouth University Research Online (Bournemouth University) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- TU/e Research Portal (submittedVersion)
- Funders
- Seventh Framework Programme (251617, ICT-2013-612944)
- Fundação para a Ciência e a Tecnologia
- COMPETE Program
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Suomen Akatemia (118653)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- concept drift, online supervised learning, adaptive learning, adaptive algorithms, algorithm evaluation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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