Using boosting to prune bagging ensembles

Gonzalo Martínez-Muñoz, Alberto Suárez
2007 Pattern Recognition Letters  
Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Pattern Recognition Letters 28.1 (2007): 156 -165 El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract Boosting is used to determine the order in which classifiers are aggregated in a bagging ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging ensemble allows
more » ... e identification of subensembles that require less memory for storage, classify faster and can improve the generalization accuracy of the original bagging ensemble. In all the classification problems investigated pruned ensembles with 20 % of the original classifiers show statistically significant improvements over bagging. In problems where boosting is superior to bagging, these improvements are not sufficient to reach the accuracy of the corresponding boosting ensembles. However, ensemble pruning preserves the performance of bagging in noisy classification tasks, where boosting often has larger generalization errors. Therefore, pruned bagging should generally be preferred to complete bagging and, if no information about the level of noise is available, it is a robust alternative to AdaBoost.
doi:10.1016/j.patrec.2006.06.018 fatcat:4274htskqrcvlkpglhtq44puwy