PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MOA Concept Drift Active Learning Strategies for Streaming Data
Indre Zliobaite, Albert Bifet, Geoff Holmes and Bernhard Pfahringer
In: WAPA 2011, October 19-21, 2011, Castro Urdiales, Spain.


We present a framework for active learning on evolving data streams, as an extension to the MOA system. In learning to classify streaming data, obtaining the true labels may require major effort and may incur excessive cost. Active learning focuses on learning an accurate model with as few labels as possible. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and classifiers need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which are typically concentrated around the decision boundary. If changes do not occur close to the boundary, they will be missed and classifiers will fail to adapt. We propose a software system that implements active learning strategies, extending the MOA framework. This software is released under the GNU GPL license.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8696
Deposited By:Albert Bifet
Deposited On:19 February 2012