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MOA: Massive Online Analysis
Abstract
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naïve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.
Type
Journal Article
Type of thesis
Series
Citation
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601-1604.
Date
2010
Publisher
Massachusetts Institute of Technology Press
Degree
Supervisors
Rights
This article has been published in the Journal of Machine Learning Research. Copyright 2010 Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer.