# DynED: Dynamic Ensemble Diversification in Data Stream Classification

arXiv: https://arxiv.org/abs/2308.10807 · HTML: https://mlsanity.com/paper/2308.10807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 21 Aug 2023.

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

- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning](https://mlsanity.com/paper/2010.00117/) (2020)
- [Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers](https://mlsanity.com/paper/1709.02925/) (2017)
- [Active Weighted Aging Ensemble for Drifted Data Stream Classification](https://mlsanity.com/paper/2112.10150/) (2021)
- [GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams](https://mlsanity.com/paper/1709.02800/) (2017)
- [On-the-Fly Ensemble Pruning in Evolving Data Streams](https://mlsanity.com/paper/2109.07611/) (2021)
- [A Broad Ensemble Learning System for Drifting Stream Classification](https://mlsanity.com/paper/2110.03540/) (2021)
- [Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization](https://mlsanity.com/paper/2211.10808/) (2022)

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

- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (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)
- [Ensemble Performance Through the Lens of Linear Independence of Classifier Votes in Data Streams](https://mlsanity.com/paper/2511.21465/) (2025)

## Abstract (arXiv)

Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering disruptive changes in the data distribution, also known as concept drift. A greater diversity of ensemble components is known to enhance prediction accuracy in such settings. Despite the diversity of components within an ensemble, not all contribute as expected to its overall performance. This necessitates a method for selecting components that exhibit high performance and diversity. We present a novel ensemble construction and maintenance approach based on MMR (Maximal Marginal Relevance) that dynamically combines the diversity and prediction accuracy of components during the process of structuring an ensemble. The experimental results on both four real and 11 synthetic datasets demonstrate that the proposed approach (DynED) provides a higher average mean accuracy compared to the five state-of-the-art baselines.