# On-the-Fly Ensemble Pruning in Evolving Data Streams

arXiv: https://arxiv.org/abs/2109.07611 · HTML: https://mlsanity.com/paper/2109.07611/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers](https://mlsanity.com/paper/1709.02925/) (2017)
- [FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection](https://mlsanity.com/paper/1810.00520/) (2018)

## Built on by (most cited first, 3 of 3)

- [DynED: Dynamic Ensemble Diversification in Data Stream Classification](https://mlsanity.com/paper/2308.10807/) (2023)
- [Mondrian Forest for Data Stream Classification Under Memory Constraints](https://mlsanity.com/paper/2205.07871/) (2022)
- [Ensemble Performance Through the Lens of Linear Independence of Classifier Votes in Data Streams](https://mlsanity.com/paper/2511.21465/) (2025)

## Abstract (arXiv)

Ensemble pruning is the process of selecting a subset of componentclassifiers from an ensemble which performs at least as well as theoriginal ensemble while reducing storage and computational costs.Ensemble pruning in data streams is a largely unexplored area ofresearch. It requires analysis of ensemble components as they arerunning on the stream, and differentiation of useful classifiers fromredundant ones. We present CCRP, an on-the-fly ensemble prun-ing method for multi-class data stream classification empoweredby an imbalance-aware fusion of class-wise component rankings.CCRP aims that the resulting pruned ensemble contains the bestperforming classifier for each target class and hence, reduces the ef-fects of class imbalance. The conducted experiments on real-worldand synthetic data streams demonstrate that different types of en-sembles that integrate CCRP as their pruning scheme consistentlyyield on par or superior performance with 20% to 90% less averagememory consumption. Lastly, we validate the proposed pruningscheme by comparing our approach against pruning schemes basedon ensemble weights and basic rank fusion methods.