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A Systematic Study of Online Class Imbalance Learning with Concept Drift

arXiv 1703.06683 ↗First posted 20 Mar 2017cs.LGcited by 28

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Abstract (arXiv)

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance. Based on the analysis, a general guideline is proposed for the development of an effective algorithm.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2017.

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Citing papers per year: 2021: 3; 2022: 10; 2023: 6; 2024: 5; 2025: 2; 2026: 2.

28 papers.

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  1. 2022From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectorscited by 82
  2. 2022A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental frameworkcited by 39
  3. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 24
  4. 2023A Comprehensive Survey on Rare Event Predictioncited by 18
  5. 2021Concept Drift Detection from Multi-Class Imbalanced Data Streamscited by 13
  6. 2022Nonstationary data stream classification with online active learning and siamese neural networkscited by 12
  7. 2022Generalization in Neural Networks: A Broad Surveycited by 11
  8. 2021Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Modelcited by 9
  9. 2023Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptationcited by 9
  10. 2022A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classificationcited by 7
  11. 2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
  12. 2022Data augmentation on-the-fly and active learning in data stream classificationcited by 5
16 more
  1. 2022ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecastingcited by 3
  2. 2022Cold Start Streaming Learning for Deep Networkscited by 3
  3. 2022Unsupervised Unlearning of Concept Drift with Autoencoderscited by 3
  4. 2023Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approachescited by 2
  5. 2024Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learningcited by 2
  6. 2024Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classificationcited by 2
  7. 2023SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streamscited by 1
  8. 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspectivecited by 1
  9. 2025SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networkscited by 1
  10. 2022Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Reportcited by 0
  11. 2023Imbalanced Data Stream Classification using Dynamic Ensemble Selectioncited by 0
  12. 2024Automating Catheterization Labs with Real-Time Perceptioncited by 0
  13. 2024Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domaincited by 0
  14. 2025Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learningcited by 0
  15. 2026Online Regime-aware Calibration for Black-box Social Simulators via Posterior-assisted Evolutionary Dynamic Optimizationcited by 0
  16. 2026Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streamscited by 0