A Bayesian random split to build ensembles of classification trees

Abstract

Random forest models [1] consist of an ensemble of randomized decision trees. It is one of the best performing classification models. With this idea in mind, in this section we introduced a random split operator based on a Bayesian approach for building a random forest. The convenience of this split method for constructing ensembles of classification trees is justified with an error bias-variance decomposition analysis. This new split operator does not clearly depend on a parameter K as its random forest's counterpart, and performs better with a lower number of trees. © 2009 Springer Berlin Heidelberg.