# A Systematic Study of Online Class Imbalance Learning with Concept Drift

arXiv: https://arxiv.org/abs/1703.06683 · HTML: https://mlsanity.com/paper/1703.06683/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 28 of 28)

- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [A Comprehensive Survey on Rare Event Prediction](https://mlsanity.com/paper/2309.11356/) (2023)
- [Concept Drift Detection from Multi-Class Imbalanced Data Streams](https://mlsanity.com/paper/2104.10228/) (2021)
- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)
- [Generalization in Neural Networks: A Broad Survey](https://mlsanity.com/paper/2209.01610/) (2022)
- [Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model](https://mlsanity.com/paper/2102.05983/) (2021)
- [Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation](https://mlsanity.com/paper/2305.08977/) (2023)
- [A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification](https://mlsanity.com/paper/2210.04949/) (2022)
- [Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice](https://mlsanity.com/paper/2102.01979/) (2021)
- [Data augmentation on-the-fly and active learning in data stream classification](https://mlsanity.com/paper/2210.06873/) (2022)
- [ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting](https://mlsanity.com/paper/2202.13471/) (2022)
- [Cold Start Streaming Learning for Deep Networks](https://mlsanity.com/paper/2211.04624/) (2022)
- [Unsupervised Unlearning of Concept Drift with Autoencoders](https://mlsanity.com/paper/2211.12989/) (2022)
- [Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches](https://mlsanity.com/paper/2302.11966/) (2023)
- [Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning](https://mlsanity.com/paper/2402.07586/) (2024)
- [Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification](https://mlsanity.com/paper/2404.15836/) (2024)
- [SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams](https://mlsanity.com/paper/2308.14845/) (2023)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks](https://mlsanity.com/paper/2504.04613/) (2025)
- [Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report](https://mlsanity.com/paper/2209.09932/) (2022)
- [Imbalanced Data Stream Classification using Dynamic Ensemble Selection](https://mlsanity.com/paper/2309.09175/) (2023)
- [Automating Catheterization Labs with Real-Time Perception](https://mlsanity.com/paper/2403.05758/) (2024)
- [Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain](https://mlsanity.com/paper/2407.10807/) (2024)
- [Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning](https://mlsanity.com/paper/2508.11353/) (2025)
- [Online Regime-aware Calibration for Black-box Social Simulators via Posterior-assisted Evolutionary Dynamic Optimization](https://mlsanity.com/paper/2601.19481/) (2026)
- [Resilient Class-Incremental Learning: on the Interplay of Drifting, Unlabelled and Imbalanced Data Streams](https://mlsanity.com/paper/2602.09681/) (2026)

## 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.