# A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

arXiv: https://arxiv.org/abs/2204.03719 · HTML: https://mlsanity.com/paper/2204.03719/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 174 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Imbalanced Continual Learning with Partitioning Reservoir Sampling](https://mlsanity.com/paper/2009.03632/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2104.06094/) (2021)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Class-Incremental Experience Replay for Continual Learning under Concept Drift](https://mlsanity.com/paper/2104.11861/) (2021)
- [A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams](https://mlsanity.com/paper/2106.09170/) (2021)
- [Online Ensemble Learning for Imbalanced Data Streams](https://mlsanity.com/paper/1310.8004/) (2013)
- [stream-learn -- open-source Python library for difficult data stream batch analysis](https://mlsanity.com/paper/2001.11077/) (2020)
- [Online Fairness-Aware Learning with Imbalanced Data Streams](https://mlsanity.com/paper/2108.06231/) (2021)
- [Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining](https://mlsanity.com/paper/1805.06368/) (2018)
- [Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification](https://mlsanity.com/paper/2102.00266/) (2021)
- [Hellinger Distance Trees for Imbalanced Streams](https://mlsanity.com/paper/1405.2278/) (2014)
- [Drift-Aware Multi-Memory Model for Imbalanced Data Streams](https://mlsanity.com/paper/2012.14791/) (2020)
- [Rebalancing Learning on Evolving Data Streams](https://mlsanity.com/paper/1911.07361/) (2019)

## Built on by (most cited first, 30 of 39)

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)
- [TopoImb: Toward Topology-level Imbalance in Learning from Graphs](https://mlsanity.com/paper/2212.08689/) (2022)
- [Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning](https://mlsanity.com/paper/2207.06084/) (2022)
- [A comprehensive analysis of concept drift locality in data streams](https://mlsanity.com/paper/2311.06396/) (2023)
- [Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants](https://mlsanity.com/paper/2402.03819/) (2024)
- [Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection](https://mlsanity.com/paper/2410.10374/) (2024)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [Resampling strategies for imbalanced regression: a survey and empirical analysis](https://mlsanity.com/paper/2507.11902/) (2025)
- [Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform](https://mlsanity.com/paper/2307.05399/) (2023)
- [Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review](https://mlsanity.com/paper/2312.16810/) (2023)
- [AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking](https://mlsanity.com/paper/2401.11250/) (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)
- [FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions](https://mlsanity.com/paper/2408.13926/) (2024)
- [Structuring the Processing Frameworks for Data Stream Evaluation and Application](https://mlsanity.com/paper/2411.06799/) (2024)
- [SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks](https://mlsanity.com/paper/2504.04613/) (2025)
- [Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing](https://mlsanity.com/paper/2606.01221/) (2026)
- [Learner-based Concept Drift Detection: Analysis and Evaluation](https://mlsanity.com/paper/2606.20216/) (2026)
- [The Influence of Multiple Classes on Learning Online Classifiers from Imbalanced and Concept Drifting Data Streams](https://mlsanity.com/paper/2210.08359/) (2022)
- [A Historical Context for Data Streams](https://mlsanity.com/paper/2310.19811/) (2023)
- [A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance](https://mlsanity.com/paper/2404.14455/) (2024)
- [Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain](https://mlsanity.com/paper/2407.10807/) (2024)
- [Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions](https://mlsanity.com/paper/2407.14381/) (2024)
- [Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection](https://mlsanity.com/paper/2409.10111/) (2024)
- [Improving Online Bagging for Complex Imbalanced Data Stream](https://mlsanity.com/paper/2410.03519/) (2024)
- [Conformal-in-the-Loop for Learning with Imbalanced Noisy Data](https://mlsanity.com/paper/2411.02281/) (2024)
- [Imbalanced malware classification: an approach based on dynamic classifier selection](https://mlsanity.com/paper/2504.00041/) (2025)
- [Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data](https://mlsanity.com/paper/2505.13116/) (2025)
- [AxelSMOTE: An Agent-Based Oversampling Algorithm for Imbalanced Classification](https://mlsanity.com/paper/2509.06875/) (2025)

## Abstract (arXiv)

Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures and benchmarks on how to evaluate these algorithms. This work proposes a standardized, exhaustive, and comprehensive experimental framework to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to a large-scale experimental study comparing state-of-the-art classifiers in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental framework is fully reproducible and easy to extend with new methods. This way, we propose a standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create complete, trustworthy, and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.