A method for pruning Bagging ensembles and its applications
Chengxian Xu · Systems Engineering - Theory & Practice · 2008
This paper presents a novel pruning method based on reordering the regressors generated by bagging, which adopts the regression tree as the base learner and selects a subset of the ordered regressors that have good prediction accuracy to construct the pruned ensemble. The experimental results show that the pruned ensemble containing about 20% of the initial pool of regressors, besides being smaller and having faster execution speed, performs better than or as well as the full bagging ensemble in the investigated regression problems.