Wang, Ching-Wei and Hunter, Andrew (2009) A low variance error boosting algorithm. Applied Intelligence, 33 (3). ISSN 0924-669X

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Abstract

This paper introduces a robust variant of AdaBoost, cw-AdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with data sets, such as microarray data, which have large numbers of features and small number of instances. The algorithm is compared with AdaBoost, Arcing and MultiBoost, using twelve gene expression datasets, using 10-fold cross validation. The new algorithm consistently achieves higher classification accuracy over all these datasets. In contrast to other AdaBoost variants, the algorithm is not susceptible to problems when a zero-error base classifier is encountered.

Item Type:	Article
Additional Information:	This paper introduces a robust variant of AdaBoost, cw-AdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with data sets, such as microarray data, which have large numbers of features and small number of instances. The algorithm is compared with AdaBoost, Arcing and MultiBoost, using twelve gene expression datasets, using 10-fold cross validation. The new algorithm consistently achieves higher classification accuracy over all these datasets. In contrast to other AdaBoost variants, the algorithm is not susceptible to problems when a zero-error base classifier is encountered.
Keywords:	Machine Learning, Boosting, Ensemble, Gene Expression Data
Subjects:	G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Bev Jones
Date Deposited:	16 Mar 2009 12:11
Last Modified:	13 Mar 2013 08:31
URI:	http://eprints.lincoln.ac.uk/id/eprint/1842

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