Online non-stationary boosting

Pocock, A., Yiapanis, P., Singer, J. , Luján, M. and Brown, G. (2010) Online non-stationary boosting. Lecture Notes in Computer Science, 5997, pp. 205-214. (doi: 10.1007/978-3-642-12127-2_21)

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Oza’s Online Boosting algorithm provides a version of AdaBoost which can be trained in an online way for stationary problems. One perspective is that this enables the power of the boosting framework to be applied to datasets which are too large to fit into memory. The online boosting algorithm assumes the data distribution to be independent and identically distributed (i.i.d.) and therefore has no provision for concept drift. We present an algorithm called Online Non-Stationary Boosting (ONSBoost) that, like Online Boosting, uses a static ensemble size without generating new members each time new examples are presented, and also adapts to a changing data distribution. We evaluate the new algorithm against Online Boosting, using the STAGGER dataset and three challenging datasets derived from a learning problem inside a parallelising virtual machine. We find that the new algorithm provides equivalent performance on the STAGGER dataset and an improvement of up to 3% on the parallelisation datasets.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Singer, Dr Jeremy
Authors: Pocock, A., Yiapanis, P., Singer, J., Luján, M., and Brown, G.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
ISSN (Online):1611-3349

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