Bagging Classification Models with Reduced Bootstrap

Abstract

Abstract.Bagging is an ensemble method proposed to improve the predictive performance of learning algorithms, being specially effective when applied to unstable predictors. It is based on the aggregation of a certain number of prediction models, each one generated from a bootstrap sample of the available training set. We introduce an alternative method for bagging classification models, motivated by the reduced bootstrap methodology, where the generated bootstrap samples are forced to have a number of distinct original observations between two values k 1 and k 2 . Five choices for k 1 and k 2 are considered, and the five resulting models are empirically studied and compared with bagging on three real data sets, employing classification trees and neural networks as the base learners. This comparison reveals for this reduced bagging technique a trend to diminish the mean and the variance of the error rate.

Bagging and boosting variants for handling classifications problems: a survey

“…Alternative bootstrap resampling scheme is also presented in Jimnez-Gamero et al (2004), which only uses a portion of the set of all possible bootstrap samples. Rafael Pino-Mejias et al (2004) proposed another alternative bootstrap procedure, namely the reduced bootstrap, which only uses a portion of the set of all possible bootstrap samples, as the sampling algorithm for bagging.…”