PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MOA: Massive Online Analysis, a framework for stream classification and clustering
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen and Thomas Seidl
In: HaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems, September 24th, 2010, Barcelona, Catalonia.

Abstract

In today’s applications, massive, evolving data streams are ubiquitous. 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 problems of scaling up the implementation of state of the art algorithms to real world dataset sizes and of making algorithms comparable in benchmark streaming settings. It contains a collection of offline and online algorithms for both classification and clustering as well as tools for evaluation. Researchers benefit from MOA by getting insights into workings and problems of different approaches, practitioners can easily compare several algorithms and apply them to real world data sets and settings. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license. Besides providing algorithms and measures for evaluation and comparison, MOA is easily extensible with new contributions and allows the creation of benchmark scenarios through storing and sharing setting files.

EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7201
Deposited By:Albert Bifet
Deposited On:09 March 2011