MOA is the most popular JAVA open source machine learning framework for data streams. A book on MOA has been published at MIT Press.
News: Launch of CapyMOA, a new fast streaming library in Python.
Machine learning library tailored for data streams. Featuring a Python API tightly integrated with MOA (Stream Learners), PyTorch (Neural Networks), and scikit-learn (Machine Learning). CapyMOA provides a fast python interface to leverage the state-of-the-art algorithms in the field of data streams. https://capymoa.org/
MOA has a very active growing community (blog). It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Developed in Java, MOA is designed to scale seamlessly for big data and complex, large-scale challenges.
MOA New Release: 24.07 (July, 2024)!
Citing MOA
If you want to refer to MOA in a publication, please cite the following JMLR paper:
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010); MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604 | BibTeX
Related Open Source Software
- CapyMOA, a fast new library for online machine learning in Python.
- RIVER, a framework for stream mining in Python. DeepRiver, a deep learning library for data streams.
- streamDM for Spark Streaming, a new framework for Spark.
- streamDM C++ , a framework in C++ for data stream ML.
- ADAMS, a novel, flexible workflow engine, is the perfect tool for maintaining MOA real-world, complex knowledge workflows.
- MEKA, an open source library for multi-label classification.