Pruning in Ordered Regression Bagging Ensembles

Daniel Hernández-Lobato, Gonzalo Martínez-Muñoz, Alberto Suárez

Abstract: An efficient procedure for pruning regression ensembles is introduced. Starting from a bagging ensemble, pruning proceeds by ordering the regressors in the original ensemble and then selecting a subset for aggregation. Ensembles of increasing size are built by including first the regressors that perform best when aggregated. This strategy gives an approximate solution to the problem of extracting from the original ensemble the minimum error subensemble, which we prove to be NP-hard. Experiments show that pruned ensembles with only 20% of the initial regressors achieve better generalization accuracies than the complete bagging ensembles. The performance of pruned ensembles is analyzed by means of the bias-variance decomposition of the error.