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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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UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,041
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,539
A Multiple Comparison Procedure for Comparing Several Treatments with a Control
Dunnett, C. W. (1955) · Journal of the American Statistical Association · doi:10.1080/01621459.1955.10501294 · Cited by 6,097
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- 10.1002/sam.v2:5/6 · DOI
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- 10.5555/1662648.1662650 · DOI
- 10.5555/1293846.1293848 · DOI
- 10.5555/3120828.3120894 · DOI
- 10.1002/rob.v23:9 · DOI
- A. Wald . 1947. Sequential Analysis . John Wiley and Sons . A. Wald. 1947. Sequential Analysis. John Wiley and Sons.
- 10.5555/2010989.2010997 · DOI
- 10.5555/2050236.2050265 · DOI
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Deep graph convolutional reinforcement learning for financial portfolio management – DeepPocket
Soleymani, F., & Paquet, E. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115127 · Cited by 93
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Rausch, T., & Dustdar, S. (2019) · 2019 IEEE International Conference on Cloud Engineering (IC2E) · doi:10.1109/ic2e.2019.00022 · Cited by 82
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Sousa, M. R., da Gama, J. M. P., & Brandão, E. F. M. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.09.055 · Cited by 82
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Pérez‐Sánchez, B., Fontenla-Romero, Ó., & Guijarro‐Berdiñas, B. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9526-2 · Cited by 84
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Ramírez‐Gallego, S., Krawczyk, B., García, S., Woźniak, M., Benítez, J. M., & Herrera, F. (2017) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2017.2700889 · Cited by 82
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Moriano, P., Hespeler, S. C., Li, M., & Mahbub, M. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11292-w · Cited by 80
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Ng, W. W. Y., Zhang, J., Lai, C. S., Pedrycz, W., Lai, L. L., & Wang, X. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2850930 · Cited by 60
Vázquez-Diosdado, J. A., Paul, V., Ellis, K. A., Coates, D., Loomba, R., & Kaler, J. (2019) · Sensors · doi:10.3390/s19143201 · Cited by 60
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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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