10 search results - page 1 / 2 » Selection of Decision Stumps in Bagging Ensembles |
ICANN
2007
Springer
13 years 11 months ago
2007
Springer
Abstract. This article presents a comprehensive study of different ensemble pruning techniques applied to a bagging ensemble composed of decision stumps. Six different ensemble p...
ROCAI
2004
Springer
13 years 10 months ago
2004
Springer
We present an empirical comparison of the AUC performance of seven supervised learning methods: SVMs, neural nets, decision trees, k-nearest neighbor, bagged trees, boosted trees,...
PRL
2008
13 years 4 months ago
2008
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the comb...
JMLR
2008
13 years 4 months ago
2008
Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are ...
PR
2010
13 years 3 months ago
2010
The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate ...