ML Anthology

Machine Learning for Data Streams with CapyMOA

Yibin Sun, Heitor Murilo Gomes, Anton Lee, Nuwan Gunasekara, Guilherme Weigert Cassales, Justin Liu, Marco Heyden, Vítor Cerqueira, Maroua Bahri, Yun Sing Koh, Bernhard Pfahringer, Albert Bifet
ECML-PKDD 2025 pp. 438-443
doi:10.1007/978-3-032-06129-4_27 /ecmlpkdd/2025/sun2025ecmlpkdd-machine/

Abstract

The exponential growth of data in recent decades has underscored the need for high-speed, real-time, and adaptive processing in machine learning. Data stream learning provides an effective framework to address this challenge. This article introduces CapyMOA, an open-source library designed specifically for data stream learning, offering powerful tools for building and deploying adaptive ML models. GitHub: https://github.com/adaptive-machine-learning/CapyMOA . Website: https://capymoa.org .

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Cite

Text

Sun et al. "Machine Learning for Data Streams with CapyMOA." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_27

Markdown

[Sun et al. "Machine Learning for Data Streams with CapyMOA." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/sun2025ecmlpkdd-machine/) doi:10.1007/978-3-032-06129-4_27

BibTeX

@inproceedings{sun2025ecmlpkdd-machine,
  title     = {{Machine Learning for Data Streams with CapyMOA}},
  author    = {Sun, Yibin and Gomes, Heitor Murilo and Lee, Anton and Gunasekara, Nuwan and Cassales, Guilherme Weigert and Liu, Justin and Heyden, Marco and Cerqueira, Vítor and Bahri, Maroua and Koh, Yun Sing and Pfahringer, Bernhard and Bifet, Albert},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2025},
  pages     = {438-443},
  doi       = {10.1007/978-3-032-06129-4_27},
  url       = {https://mlanthology.org/ecmlpkdd/2025/sun2025ecmlpkdd-machine/}
}