PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning the tree augmented naive bayes classifier from incomplete datasets
Olivier François and Philippe Leray
In: The third European Workshop on Probabilistic Graphical Models PGM'06, Prague, Czech Republic(2006).

Abstract

The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid or diagnosis, in particular thanks to its inference capabilities, even when data are incomplete. For classification tasks, Naive Bayes and Augmented Naive Bayes classifiers have shown excellent performances. Learning a Naive Bayes classifier from incomplete datasets is not difficult as only parameter learning has to be performed. But there are not many methods to efficiently learn Tree Augmented Naive Bayes classifiers from incomplete datasets. In this paper, we take up the structural em algorithm principle introduced by (Friedman, 1997) to propose an algorithm to answer this question.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3772
Deposited By:Philippe Leray
Deposited On:21 February 2008