A Bayesian Random Split to Build Ensembles of Classification Trees

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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.

This work has been jointly supported by Spanish Ministry of Education and Science under project TIN2007-67418-C03-03, by European Regional Development Fund (FEDER), by the Spanish research programme Consolider Ingenio 2010: MIPRCV (CSD2007-00018), and by the FPU scholarship programme (AP2004-4678).