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Smoclust: synthetic minority oversampling based on stream clustering for evolving data streams

Chiu, Chun Wai; Minku, Leandro L.


Chun Wai Chiu

Leandro L. Minku


Many real-world data stream applications not only suffer from concept drift but also class imbalance. Yet, very few existing studies investigated this joint challenge. Data difficulty factors, which have been shown to be key challenges in class imbalanced data streams, are not taken into account by existing approaches when learning class imbalanced data streams. In this work, we propose a drift adaptable oversampling strategy to synthesise minority class examples based on stream clustering. The motivation is that stream clustering methods continuously update themselves to reflect the characteristics of the current underlying concept, including data difficulty factors. This nature can potentially be used to compress past information without caching data in the memory explicitly. Based on the compressed information, synthetic examples can be created within the region that recently generated new minority class examples. Experiments with artificial and real-world data streams show that the proposed approach can handle concept drift involving different minority class decomposition better than existing approaches, especially when the data stream is severely class imbalanced and presenting high proportions of safe and borderline minority class examples.


Chiu, C. W., & Minku, L. L. (2024). Smoclust: synthetic minority oversampling based on stream clustering for evolving data streams. Machine Learning, 113(7), 4671-4721.

Journal Article Type Article
Acceptance Date Oct 3, 2023
Online Publication Date Dec 18, 2023
Publication Date Jul 1, 2024
Deposit Date Jun 13, 2024
Journal Machine Learning
Print ISSN 0885-6125
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 113
Issue 7
Pages 4671-4721
Keywords Concept drift, Data difficulty factors, Class imbalance, Data streams, Synthetic data, Stream clustering
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