Machine Learning for Data Streams with CapyMOA

Yibin Sun; Heitor Murilo Gomes; Anton Lee; Nuwan Gunasekara; Guilherme Weigert Cassales; Jia Justin Liu; Marco Heyden; Vitor Cerqueira; Maroua Bahri; Yun Sing Koh; Bernhard Pfahringer; Albert Bifet

doi:10.1007/978-3-032-06129-4_27

Machine Learning for Data Streams with CapyMOA

Yibin Sun
, Heitor Murilo Gomes
, Anton Lee
, Nuwan Gunasekara
, Guilherme Weigert Cassales
, Jia Justin Liu
, Marco Heyden
, Vitor Cerqueira
, Maroua Bahri
, Yun Sing Koh
, Bernhard Pfahringer
, Albert Bifet

University of Waikato
Victoria University of Wellington
Halmstad University
Institute of Meteorology and Climate Research
Ipatimup Diagnósticos
Sorbonne Université
University of Auckland

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings
rédacteurs en chef	Inês Dutra, Alípio M. Jorge, Carlos Soares, João Gama, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Pedro H. Abreu
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	438-443
Nombre de pages	6
ISBN (imprimé)	9783032061287
Les DOIs	https://doi.org/10.1007/978-3-032-06129-4_27
état	Publié - 1 janv. 2026
Evénement	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025 - Porto, Portugal Durée: 15 sept. 2025 → 19 sept. 2025

Série de publications

Nom	Lecture Notes in Computer Science
Volume	16022
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025
Pays/Territoire	Portugal
La ville	Porto
période	15/09/25 → 19/09/25

Accès au document

10.1007/978-3-032-06129-4_27

Autres fichiers et liens

Lien vers la publication dans Scopus

Contient cette citation

Sun, Y., Gomes, H. M., Lee, A., Gunasekara, N., Weigert Cassales, G., Liu, J. J., Heyden, M., Cerqueira, V., Bahri, M., Koh, Y. S., Pfahringer, B., & Bifet, A. (2026). Machine Learning for Data Streams with CapyMOA. Dans I. Dutra, A. M. Jorge, C. Soares, J. Gama, M. Pechenizkiy, P. Cortez, S. Pashami, A. Pasquali, N. Moniz, & P. H. Abreu (eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings (p. 438-443). (Lecture Notes in Computer Science; Vol 16022). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-06129-4_27

Sun, Yibin ; Gomes, Heitor Murilo ; Lee, Anton et al. / Machine Learning for Data Streams with CapyMOA. Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings. Editeur / Inês Dutra ; Alípio M. Jorge ; Carlos Soares ; João Gama ; Mykola Pechenizkiy ; Paulo Cortez ; Sepideh Pashami ; Arian Pasquali ; Nuno Moniz ; Pedro H. Abreu. Springer Science and Business Media Deutschland GmbH, 2026. p. 438-443 (Lecture Notes in Computer Science).

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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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Sun, Y, Gomes, HM, Lee, A, Gunasekara, N, Weigert Cassales, G, Liu, JJ, Heyden, M, Cerqueira, V, Bahri, M, Koh, YS, Pfahringer, B & Bifet, A 2026, Machine Learning for Data Streams with CapyMOA. Dans I Dutra, AM Jorge, C Soares, J Gama, M Pechenizkiy, P Cortez, S Pashami, A Pasquali, N Moniz & PH Abreu (eds), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings. Lecture Notes in Computer Science, VOL. 16022, Springer Science and Business Media Deutschland GmbH, p. 438-443, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, Porto, Portugal, 15/09/25. https://doi.org/10.1007/978-3-032-06129-4_27

Machine Learning for Data Streams with CapyMOA. / Sun, Yibin; Gomes, Heitor Murilo; Lee, Anton et al.
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings. Ed. / Inês Dutra; Alípio M. Jorge; Carlos Soares; João Gama; Mykola Pechenizkiy; Paulo Cortez; Sepideh Pashami; Arian Pasquali; Nuno Moniz; Pedro H. Abreu. Springer Science and Business Media Deutschland GmbH, 2026. p. 438-443 (Lecture Notes in Computer Science; Vol 16022).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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AU - Gomes, Heitor Murilo

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AU - Gunasekara, Nuwan

AU - Weigert Cassales, Guilherme

AU - Liu, Jia Justin

AU - Heyden, Marco

AU - Cerqueira, Vitor

AU - Bahri, Maroua

AU - Koh, Yun Sing

AU - Pfahringer, Bernhard

AU - Bifet, Albert

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026/1/1

Y1 - 2026/1/1

N2 - 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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KW - Data Streams

KW - Machine Learning

KW - Online Continual Learning

KW - Open-source

KW - Semi-supervised Learning

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DO - 10.1007/978-3-032-06129-4_27

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T3 - Lecture Notes in Computer Science

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BT - Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings

A2 - Dutra, Inês

A2 - Jorge, Alípio M.

A2 - Soares, Carlos

A2 - Gama, João

A2 - Pechenizkiy, Mykola

A2 - Cortez, Paulo

A2 - Pashami, Sepideh

A2 - Pasquali, Arian

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PB - Springer Science and Business Media Deutschland GmbH

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Sun Y, Gomes HM, Lee A, Gunasekara N, Weigert Cassales G, Liu JJ et al. Machine Learning for Data Streams with CapyMOA. Dans Dutra I, Jorge AM, Soares C, Gama J, Pechenizkiy M, Cortez P, Pashami S, Pasquali A, Moniz N, Abreu PH, rédacteurs en chef, Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2026. p. 438-443. (Lecture Notes in Computer Science). doi: 10.1007/978-3-032-06129-4_27