mlsanityData to 14 Oct 2026

Characterizing Concept Drift

arXiv 1511.03816 ↗First posted 12 Nov 2015cs.LG cs.AIcited by 94

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Abstract (arXiv)

Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called concept drift. However, the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis. In particular, while some qualitative drift categorizations have been proposed, few have been formally defined, and the quantitative descriptions required for precise and objective understanding of learner performance have not existed. We present the first comprehensive framework for quantitative analysis of drift. This supports the development of the first comprehensive set of formal definitions of types of concept drift. The formal definitions clarify ambiguities and identify gaps in previous definitions, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.

Key facts. Cited by 94 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2015.

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Citing papers per year: 2021: 22; 2022: 16; 2023: 18; 2024: 13; 2025: 14; 2026: 11.

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  1. 2022From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectorscited by 83
  2. 2022Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practicescited by 55
  3. 2022A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental frameworkcited by 39
  4. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  5. 2022DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptationcited by 36
  6. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  7. 2021DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networkscited by 19
  8. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 18
  9. 2021A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streamscited by 15
  10. 2022WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Datacited by 13
  11. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 13
  12. 2021On the Robustness of Domain Constraintscited by 11
38 more
  1. 2021Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classescited by 11
  2. 2022Online AutoML: An adaptive AutoML framework for online learningcited by 11
  3. 2023A comprehensive analysis of concept drift locality in data streamscited by 10
  4. 2024Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Valuescited by 10
  5. 2021Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Modelcited by 9
  6. 2021LEAF: Navigating Concept Drift in Cellular Networkscited by 9
  7. 2022A Human-Centric Take on Model Monitoringcited by 9
  8. 2021Active Weighted Aging Ensemble for Drifted Data Stream Classificationcited by 8
  9. 2022Change Detection for Local Explainability in Evolving Data Streamscited by 8
  10. 2023DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networkscited by 8
  11. 2024Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environmentscited by 8
  12. 2022A unified framework for dataset shift diagnosticscited by 7
  13. 2023One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environmentscited by 7
  14. 2021Using AntiPatterns to avoid MLOps Mistakescited by 5
  15. 2022Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selectioncited by 5
  16. 2022Learning from Data Streams: An Overview and Updatecited by 5
  17. 2023Efficient Concept Drift Handling for Batch Android Malware Detection Modelscited by 5
  18. 2023Quilt: Robust Data Segment Selection against Concept Driftscited by 5
  19. 2023DSAP: Analyzing Bias Through Demographic Comparison of Datasetscited by 5
  20. 2024Prompted Contextual Vectors for Spear-Phishing Detectioncited by 5
  21. 2021ACDC: Online Unsupervised Cross-Domain Adaptationcited by 4
  22. 2022Standardized Evaluation of Machine Learning Methods for Evolving Data Streamscited by 4
  23. 2023Handling Concept Drift in Global Time Series Forecastingcited by 4
  24. 2023Test & Evaluation Best Practices for Machine Learning-Enabled Systemscited by 4
  25. 2025Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detectioncited by 4
  26. 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streamscited by 3
  27. 2023Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmarkcited by 3
  28. 2024How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detectioncited by 3
  29. 2021More Than Meets The Eye: Semi-supervised Learning Under Non-IID Datacited by 2
  30. 2021Learning Football Body-Orientation as a Matter of Classificationcited by 2
  31. 2021Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streamscited by 2
  32. 2021Scanflow: A multi-graph framework for Machine Learning workflow management, supervision, and debuggingcited by 2
  33. 2022Lifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learningcited by 2
  34. 2022Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detectioncited by 2
  35. 2022Dealing with Drift of Adaptation Spaces in Learning-based Self-Adaptive Systems using Lifelong Self-Adaptationcited by 2
  36. 2023Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysiscited by 2
  37. 2023FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deploymentscited by 2
  38. 2023Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddingscited by 2

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