# Online Ensemble Learning for Imbalanced Data Streams

arXiv: https://arxiv.org/abs/1310.8004 · HTML: https://mlsanity.com/paper/1310.8004/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2013.

## Built on by (most cited first, 12 of 12)

- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [Preventing Discriminatory Decision-making in Evolving Data Streams](https://mlsanity.com/paper/2302.08017/) (2023)
- [Online Fairness-Aware Learning with Imbalanced Data Streams](https://mlsanity.com/paper/2108.06231/) (2021)
- [Generalized Incremental Learning under Concept Drift across Evolving Data Streams](https://mlsanity.com/paper/2506.05736/) (2025)
- [Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection](https://mlsanity.com/paper/2410.10374/) (2024)
- [AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification](https://mlsanity.com/paper/2209.08309/) (2022)
- [PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment](https://mlsanity.com/paper/2503.15581/) (2025)
- [A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios](https://mlsanity.com/paper/2103.13823/) (2021)
- [SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams](https://mlsanity.com/paper/2308.14845/) (2023)
- [A Review of Machine Learning Techniques in Imbalanced Data and Future Trends](https://mlsanity.com/paper/2310.07917/) (2023)
- [Discrimination and Class Imbalance Aware Online Naive Bayes](https://mlsanity.com/paper/2211.04812/) (2022)
- [Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning](https://mlsanity.com/paper/2508.11353/) (2025)

## Abstract (arXiv)

While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms. Within this framework, two separately developed research areas are bridged together, and a batch of theoretically sound online cost-sensitive bagging and online cost-sensitive boosting algorithms are first proposed. Unlike other online cost-sensitive learning algorithms lacking theoretical analysis of asymptotic properties, the convergence of the proposed algorithms is guaranteed under certain conditions, and the experimental evidence with benchmark data sets also validates the effectiveness and efficiency of the proposed methods.