MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. It contains collection of offline and online for both classification and clustering as well as tools for evaluation. In particular, for classification it implements boosting, bagging, and Hoeffding Trees, all with and without Na

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl
• Keywords: Algorithms, JMLR 2010, Real World, Real World Dataset