# Adaptive XGBoost for Evolving Data Streams

arXiv: https://arxiv.org/abs/2005.07353 · HTML: https://mlsanity.com/paper/2005.07353/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 8 of 8)

- [Are Concept Drift Detectors Reliable Alarming Systems? -- A Comparative Study](https://mlsanity.com/paper/2211.13098/) (2022)
- [Dynamic Model Tree for Interpretable Data Stream Learning](https://mlsanity.com/paper/2203.16181/) (2022)
- [Tackling Concept Shift in Text Classification using Entailment-style Modeling](https://mlsanity.com/paper/2311.03320/) (2023)
- [Confident Sinkhorn Allocation for Pseudo-Labeling](https://mlsanity.com/paper/2206.05880/) (2022)
- [An Attention-based Feature Memory Design for Energy-Efficient Continual Learning](https://mlsanity.com/paper/2510.04660/) (2025)
- [Online learning to accelerate nonlinear PDE solvers: applied to multiphase porous media flow](https://mlsanity.com/paper/2504.18414/) (2025)
- [An Optimised Greedy-Weighted Ensemble Framework for Financial Loan Default Prediction](https://mlsanity.com/paper/2603.18927/) (2026)
- [Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference](https://mlsanity.com/paper/2605.31239/) (2026)

## Abstract (arXiv)

Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present an adaptation of XGB for classification of evolving data streams. In this setting, new data arrives over time and the relationship between the class and the features may change in the process, thus exhibiting concept drift. The proposed method creates new members of the ensemble from mini-batches of data as new data becomes available. The maximum ensemble size is fixed, but learning does not stop when this size is reached because the ensemble is updated on new data to ensure consistency with the current concept. We also explore the use of concept drift detection to trigger a mechanism to update the ensemble. We test our method on real and synthetic data with concept drift and compare it against batch-incremental and instance-incremental classification methods for data streams.