Selection of tree-based classifiers with the bootstrap 632+ rule

Abstract

This paper introduces a novel model selection procedure for tree-based classifiers. The method is based on the bootstrap 632+ rule recently proposed by Efron and Tibshirani. The rule allows selecting compact, non-overfitting classification trees by reweighting the contributions of the resubstitution and standard bootstrap estimated errors. The proposed method is applied in a medical entomology problem for modeling the risk of parasite presence.