Title: Cost-complexity pruning of random forests,
Abstract: Random forests perform bootstrap-aggregation by sampling the training samples
with replacement. This enables the evaluation of out-of-bag error which serves
as a internal cross-validation mechanism. Our motivation lies in using the
unsampled training samples to improve each decision tree in the ensemble. We
study the effect of using the out-of-bag samples to improve the generalization
error first of the decision trees and second the random forest by post-pruning.
A preliminary empirical study on four UCI repository datasets show consistent
decrease in the size of the forests without considerable loss in accuracy.