# Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

arXiv: https://arxiv.org/abs/1709.02457 · HTML: https://mlsanity.com/paper/1709.02457/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 10 of 10)

- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [A comprehensive analysis of concept drift locality in data streams](https://mlsanity.com/paper/2311.06396/) (2023)
- [Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model](https://mlsanity.com/paper/2102.05983/) (2021)
- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [When to retrain a machine learning model](https://mlsanity.com/paper/2505.14903/) (2025)
- [IncA-DES: An incremental and adaptive dynamic ensemble selection approach using online K-d tree neighborhood search for data streams with concept drift](https://mlsanity.com/paper/2507.12573/) (2025)
- [Concept Drift Visualization of SVM with Shifting Window](https://mlsanity.com/paper/2406.13754/) (2024)
- [Online Deep Learning based on Auto-Encoder](https://mlsanity.com/paper/2201.07383/) (2022)
- [Implicit Concept Drift Detection for Multi-label Data Streams](https://mlsanity.com/paper/2202.00070/) (2022)

## Abstract (arXiv)

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the mboxTornado framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the mboxFHDDMS and mboxFHDDMSadd approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our mboxFHDDMS variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.