PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

BAYESIAN NETWORK STRUCTURAL LEARNING AND INCOMPLETE DATA
Philippe Leray and Olivier François
In: International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2005), 15-17 Jun 2005, Espoo, Finland.

Abstract

The Bayesian network formalismis becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, evenwhen data are incomplete. Besides, estimating the parameters of a fixed-structure Bayesian network is easy. However, very few methods are capable of using incomplete cases as a base to determine the structure of a Bayesian network. In this paper, we take up the structural EM algorithm principle [9, 10] to propose an algorithm which extends the Maximum Weight Spanning Tree algorithm to deal with incomplete data. We also propose to use this extension in order to (1) speed up the structural EM algorithm or (2) in classification tasks extend the Tree Augmented Naive classifier in order to deal with incomplete data.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:993
Deposited By:Philippe Leray
Deposited On:19 June 2005