Bayes-N: An algorithm for learning Bayesian networks from data using local measures of information gain applied to classification problems

Abstract

Bayes-N is an algorithm for Bayesian network learning from data based on local measures of information gain, applied to problems in which there is a given dependent or class variable and a set of independent or explanatory variables from which we want to predict the class variable on new cases. Given this setting, Bayes-N induces an ancestral ordering of all the variables generating a directed acyclic graph in which the class variable is a sink variable, with a subset of the explanatory variables as its parents. It is shown that classification using this variables as predictors performs better than the naive bayes classifier, and at least as good as other algorithms that learn Bayesian networks such as K2, PC and Bayes-9. It is also shown that the MDL measure of the networks generated by Bayes-N is comparable to those obtained by these other algorithms.