Double-bagging: combining classifiers by bootstrap aggregation

Abstract

The combination of classifiers leads to substantial reduction of misclassification error in a wide range of applications and benchmark problems. We suggest using an out-of-bag sample for combining different classifiers. In our setup, a linear discriminant analysis is performed using the observations in the out-of-bag sample, and the corresponding discriminant variables computed for the observations in the bootstrap sample are used as additional predictors for a classification tree. Two classifiers are combined and therefore method and variable selection bias is no problem for the corresponding estimate of misclassification error, the need of an additional test sample disappears. Moreover, the procedure performs comparable to the best classifiers used in a number of artificial examples and applications.