Chapter

Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Volume 5590 of the series Lecture Notes in Computer Science pp 469-480

A Bayesian Random Split to Build Ensembles of Classification Trees

  • Andrés CanoAffiliated withDepartment of Computer Science and Artificial Intelligence, University of Granada
  • , Andrés R. MasegosaAffiliated withDepartment of Computer Science and Artificial Intelligence, University of Granada
  • , Serafín MoralAffiliated withDepartment of Computer Science and Artificial Intelligence, University of Granada

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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.