# Online Boosting Adaptive Learning under Concept Drift for Multistream Classification

arXiv: https://arxiv.org/abs/2312.10841 · HTML: https://mlsanity.com/paper/2312.10841/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation](https://mlsanity.com/paper/2201.04038/) (2022)
- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)
- [Learngene: From Open-World to Your Learning Task](https://mlsanity.com/paper/2106.06788/) (2021)
- [Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream](https://mlsanity.com/paper/2206.04792/) (2022)
- [Multi-Source Transfer Learning for Non-Stationary Environments](https://mlsanity.com/paper/1901.02052/) (2019)
- [ATL: Autonomous Knowledge Transfer from Many Streaming Processes](https://mlsanity.com/paper/1910.03434/) (2019)
- [Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model](https://mlsanity.com/paper/2102.05983/) (2021)
- [Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation](https://mlsanity.com/paper/2004.05810/) (2020)
- [Automatic Online Multi-Source Domain Adaptation](https://mlsanity.com/paper/2109.01996/) (2021)
- [Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation](https://mlsanity.com/paper/2209.12029/) (2022)

## Built on by (most cited first, 17 of 17)

- [Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards](https://mlsanity.com/paper/2405.13459/) (2024)
- [Learning Robust Spectral Dynamics for Temporal Domain Generalization](https://mlsanity.com/paper/2505.12585/) (2025)
- [Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning](https://mlsanity.com/paper/2508.01598/) (2025)
- [Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2410.18478/) (2024)
- [Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments](https://mlsanity.com/paper/2411.00186/) (2024)
- [Early Concept Drift Detection via Prediction Uncertainty](https://mlsanity.com/paper/2412.11158/) (2024)
- [Generalized Incremental Learning under Concept Drift across Evolving Data Streams](https://mlsanity.com/paper/2506.05736/) (2025)
- [Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning](https://mlsanity.com/paper/2505.13081/) (2025)
- [Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.16606/) (2025)
- [Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning](https://mlsanity.com/paper/2402.07586/) (2024)
- [TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG](https://mlsanity.com/paper/2512.07515/) (2025)
- [Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences](https://mlsanity.com/paper/2409.13857/) (2024)
- [Multi-Label Transfer Learning in Non-Stationary Data Streams](https://mlsanity.com/paper/2509.08181/) (2025)
- [OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average](https://mlsanity.com/paper/2512.12785/) (2025)
- [Autonomous Drift Learning in Data Streams: A Unified Perspective](https://mlsanity.com/paper/2605.01295/) (2026)
- [FreKoo++: Learning Continuous Spectral Dynamics for Temporal Domain Generalization](https://mlsanity.com/paper/2608.22224/) (2026)
- [Concept drift mitigation through community and spectral graph analysis for the detection of cyberattacks in network traffic](https://mlsanity.com/paper/2609.09442/) (2026)

## Abstract (arXiv)

Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the temporal dynamic relationships between these streams, leading to the issue of negative transfer arising from irrelevant data. In this paper, we propose a novel Online Boosting Adaptive Learning (OBAL) method that effectively addresses this limitation by adaptively learning the dynamic correlation among different streams. Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy. During the online process, we employ a Gaussian Mixture Model-based weighting mechanism, which is seamlessly integrated with the acquired correlations via AdaCOSA to effectively handle asynchronous drift. This approach significantly improves the predictive performance and stability of the target stream. We conduct comprehensive experiments on several synthetic and real-world data streams, encompassing various drifting scenarios and types. The results clearly demonstrate that OBAL achieves remarkable advancements in addressing multistream classification problems by effectively leveraging positive knowledge derived from multiple sources.