Applied Intelligence

, Volume 33, Issue 3, pp 357–369

A low variance error boosting algorithm


DOI: 10.1007/s10489-009-0172-0

Cite this article as:
Wang, CW. & Hunter, A. Appl Intell (2010) 33: 357. doi:10.1007/s10489-009-0172-0


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.


Boosting Bagging Arcing Multiboost Ensemble machine learning Random resampling weighted instances Variance error Bias error 

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.University of LincolnLincolnUK

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