# Queue-based Resampling for Online Class Imbalance Learning

arXiv: https://arxiv.org/abs/1809.10388 · HTML: https://mlsanity.com/paper/1809.10388/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)
- [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)
- [Data augmentation on-the-fly and active learning in data stream classification](https://mlsanity.com/paper/2210.06873/) (2022)
- [Unsupervised Unlearning of Concept Drift with Autoencoders](https://mlsanity.com/paper/2211.12989/) (2022)
- [SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks](https://mlsanity.com/paper/2504.04613/) (2025)
- [Drift-Aware Variational Autoencoder-based Anomaly Detection with Two-level Ensembling](https://mlsanity.com/paper/2602.12976/) (2026)

## Abstract (arXiv)

Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance status. Little work exists that addresses these challenges and in this paper we introduce queue-based resampling, a novel algorithm that successfully addresses the co-existence of class imbalance and concept drift. The central idea of the proposed resampling algorithm is to selectively include in the training set a subset of the examples that appeared in the past. Results on two popular benchmark datasets demonstrate the effectiveness of queue-based resampling over state-of-the-art methods in terms of learning speed and quality.