The DOI matches its Crossref record; 165 of the 171 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Research works printed but not linked: 9
- 10.1002/sam.v2:5/6 · DOI
- 10.5555/647458.728399 · DOI
- 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
Cited by
Sorted by citations
Items 1,001–1,007 of 1,007
AdaEvo: Edge-Assisted Continuous and Timely DNN Model Evolution for Mobile Devices
Wang, L., Yu, Z., Yu, H., Liu, S., Xie, Y., Guo, B., & Liu, Y. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3316388 · Cited by 14
A crowdsourcing-based incremental learning framework for automated essays scoring
Bai, H., & Hui, S. C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121755 · Cited by 14
Brouwer, M., Hofstee, L., van den Brand, S. A. G. E., Teijema, J. J., Ferdinands, G., de Boer, J., Weijdema, F., Kramer, B., Wiers, R., Bockting, C., et al. (2022) · doi:10.31234/osf.io/j6nqz · Cited by 11
A benchmarking framework and dataset for learning to defer in human-AI decision-making
Alves, J. V., Leitão, D., Jesus, S., Sampaio, M. O. P., Liébana, J., Saleiro, P., Figueiredo, M. A. T., & Bizarro, P. (2025) · Scientific Data · doi:10.1038/s41597-025-04664-y · Cited by 10
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems
Pagan, N., Baumann, J., Elokda, E., De Pasquale, G., Bolognani, S., & Hannák, A. (2023) · arXiv · doi:10.48550/arxiv.2305.06055 · Cited by 4
Data Organisation for Efficient Pattern Retrieval: Indexing, Storage, and Access Structures
Koukaras, P., & Tjortjis, C. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9100258 · Cited by 3
“This feature becomes crucial in concept drift situations or analysis across different temporal contexts, where outdated patterns need to be removed or down-ranked [67].”
Towards Out-Of-Distribution Generalization: A Survey
Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv · doi:10.48550/arxiv.2108.13624 · Cited by 253
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
