Applied Intelligence

, Volume 33, Issue 3, pp 357-369

First online:

A low variance error boosting algorithm

  • Ching-Wei WangAffiliated withUniversity of Lincoln Email author 
  • , Andrew HunterAffiliated withUniversity of Lincoln

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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