# Active Weighted Aging Ensemble for Drifted Data Stream Classification

arXiv: https://arxiv.org/abs/2112.10150 · HTML: https://mlsanity.com/paper/2112.10150/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 74 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [stream-learn -- open-source Python library for difficult data stream batch analysis](https://mlsanity.com/paper/2001.11077/) (2020)
- [Tackling Virtual and Real Concept Drifts: An Adaptive Gaussian Mixture Model](https://mlsanity.com/paper/2102.05983/) (2021)

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

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [DynED: Dynamic Ensemble Diversification in Data Stream Classification](https://mlsanity.com/paper/2308.10807/) (2023)
- [Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification](https://mlsanity.com/paper/2404.15836/) (2024)
- [A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection](https://mlsanity.com/paper/2404.16656/) (2024)
- [Structuring the Processing Frameworks for Data Stream Evaluation and Application](https://mlsanity.com/paper/2411.06799/) (2024)
- [SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks](https://mlsanity.com/paper/2504.04613/) (2025)
- [Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain](https://mlsanity.com/paper/2407.10807/) (2024)
- [Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation](https://mlsanity.com/paper/2410.04183/) (2024)

## Abstract (arXiv)

One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon is required to adapt the classifier to the changing probabilistic characteristics. One of the significant problems in implementing such a solution is the access to data labels. It is usually costly, so to minimize the expenses related to this process, learning strategies based on semi-supervised learning are proposed, e.g., employing active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. This paper proposes a novel chunk-based method for non-stationary data streams based on classifier ensemble learning and an active learning strategy considering a limited budget that can be successfully applied to any data stream classification algorithm. The proposed method has been evaluated through computer experiments using both real and generated data streams. The results confirm the high quality of the proposed algorithm over state-of-the-art methods.