PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MOA: Massive Online Analysis
Albert Bifet, Geoff Holmes, Richard Kirkby and Berhard Pfahringer
Journal of Machine Learning Research (JMLR) Volume 11, Number May, pp. 1601-1604, 2010.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7194
Deposited By:Albert Bifet
Deposited On:09 March 2011