stream-learn -- open-source Python library for difficult data stream batch analysis
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Abstract (arXiv)
stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows to produce a synthetic data stream that may incorporate each of the three main concept drift types (i.e. sudden, gradual and incremental drift) in their recurring or non-recurring versions. The package allows conducting experiments following established evaluation methodologies (i.e. Test-Then-Train and Prequential). In addition, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-art chunk-based and online classifier ensembles. To improve computational efficiency, package utilises its own implementations of prediction metrics for imbalanced binary classification tasks.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2020.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 4; 2025: 0; 2026: 2.
12 papers.
- 2022A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental frameworkcited by 39
- 2021Active Weighted Aging Ensemble for Drifted Data Stream Classificationcited by 8
- 2022Tracking changes using Kullback-Leibler divergence for the continual learningcited by 6
- 2021Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classificationcited by 4
- 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streamscited by 3
- 2024Unsupervised Concept Drift Detection based on Parallel Activations of Neural Networkcited by 2
- 2024Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classificationcited by 2
- 2026Unlearning-based sliding window for continual learning under concept driftcited by 1
- 2021Employing chunk size adaptation to overcome concept driftcited by 0
- 2024Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domaincited by 0
- 2024Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservationcited by 0
- 2026How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluationcited by 0
- 2026How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation29 May 2026
- 2026Unlearning-based sliding window for continual learning under concept drift15 Mar 2026
- 2024Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation5 Oct 2024
- 2024Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain15 Jul 2024
- 2024Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification24 Apr 2024
- 2024Unsupervised Concept Drift Detection based on Parallel Activations of Neural Network11 Apr 2024
- 2023OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams29 Aug 2023
- 2022Tracking changes using Kullback-Leibler divergence for the continual learning10 Oct 2022
- 2022A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework7 Apr 2022
- 2021Active Weighted Aging Ensemble for Drifted Data Stream Classification19 Dec 2021
- 2021Employing chunk size adaptation to overcome concept drift25 Oct 2021
- 2021Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification30 Jan 2021