Machine learning for data streams with CapyMOA
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This is an author’s accepted version of a conference paper published in the proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track and Demo Track (ECML PKDD 2025). © 2025 Springer.
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, H. M., Lee, A., Gunasekara, N., Weigert Cassales, G., LIU, J., Heyden, M., Cerqueira, V., Bahri, M., Koh, Y. S., Pfahringer, B., & Bifet, A. (2026). Machine learning for data streams with CapyMOA. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track and Demo Track (ECML PKDD 2025) Part X (pp. 438-443). Springer. https://doi.org/10.1007/978-3-032-06129-4_27
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Springer