Wrappers have recently been used to obtain parameter optimizations for learning algorithms. In this paper we investigate the
use of a wrapper for estimating the correct number of boosting ensembles in the presence of class noise. Contrary to the naive
approach that would be quadratic in the number of boosting iterations, the incremental algorithm described is linear.
Additionally, directly using the k-sized ensembles generated during k-fold cross-validation search for prediction usually
results in further improvements in classification performance. This improvement can be attributed to the reduction of variance
due to averaging k ensembles instead of using only one ensemble. Consequently, cross-validation in the way we use it here,
termed wrapping, can be viewed as yet another ensemble learner similar in spirit to bagging but also somewhat related to stacking.
Keywords machine learning