Sun, Q. & Pfahringer, B. (2012). Bagging ensemble selection for regression. In Lecture Notes in Computer Science 2012, Volume 7691, Al 2012: Advances in Artificial Intelligence. Pp 695-706.
Permanent Research Commons link: http://hdl.handle.net/10289/6940
Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classiﬁcation problems have shown that using random trees as base classiﬁers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by the promising results in classiﬁcation, this paper examines the predictive performance of the BES-OOB strategy for regression problems. Our results show that the BES-OOB strategy outperforms Stochastic Gradient Boosting and Bagging when using regression trees as the base learners. Our results also suggest that the advantage of using a diverse model library becomes clear when the model library size is relatively large. We also present encouraging results indicating that the non negative least squares algorithm is a viable approach for pruning an ensemble of ensembles.
This is the author's accepted version. The original publication is available at www.springerlink.com. Copyright Springer-Verlag Berlin Heidelberg 2012.