# A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification

arXiv: https://arxiv.org/abs/2210.04949 · HTML: https://mlsanity.com/paper/2210.04949/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Online Learning With Adaptive Rebalancing in Nonstationary Environments](https://mlsanity.com/paper/2009.11942/) (2020)
- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)
- [Queue-based Resampling for Online Class Imbalance Learning](https://mlsanity.com/paper/1809.10388/) (2018)
- [Data-efficient Online Classification with Siamese Networks and Active Learning](https://mlsanity.com/paper/2010.01659/) (2020)

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

- [Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation](https://mlsanity.com/paper/2305.08977/) (2023)
- [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)
- [Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification](https://mlsanity.com/paper/2404.02572/) (2024)
- [Online Detection of Water Contamination Under Concept Drift](https://mlsanity.com/paper/2501.02107/) (2025)
- [SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks](https://mlsanity.com/paper/2504.04613/) (2025)
- [Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks](https://mlsanity.com/paper/2508.16336/) (2025)

## Abstract (arXiv)

In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour). These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models that become obsolete over time, and inadequate to solve the task at hand. Learning in presence of concept drift aims at designing machine and deep learning models that are able to track and adapt to concept drift. Typically, techniques to handle concept drift are either active or passive, and traditionally, these have been considered to be mutually exclusive. Active techniques use an explicit drift detection mechanism, and re-train the learning algorithm when concept drift is detected. Passive techniques use an implicit method to deal with drift, and continually update the model using incremental learning. Differently from what present in the literature, we propose a hybrid alternative which merges the two approaches, hence, leveraging on their advantages. The proposed method called Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines and state-of-the-art methods in terms of learning quality and speed; we experiment how it is effective under severe class imbalance levels too.