Publication

Back to overview

Likelihood-Based Robust Classification with Bayesian Networks

Type of publication Peer-reviewed
Publikationsform Contribution to book (peer-reviewed)
Author Antonucci A., Cattaneo M.E.G.V., Corani G.,
Project Multi Model Inference for dealing with uncertainty in environmental models
Show all

Contribution to book (peer-reviewed)

Book Advances in Computational Intelligence - Communications in Computer and Information Science
Editor , Greco S.; Bouchon-Meunier B.; Coletti G.; Fedrizzi M.; Matarazzo B.; Yager R.R.
Publisher Springer, Berlin
Page(s) 491 - 500
ISBN 978-3-642-31717-0
Title of proceedings Advances in Computational Intelligence - Communications in Computer and Information Science
DOI 10.1007/978-3-642-31718-7_51

Abstract

Bayesian networks are commonly used for classification: a structural learning algorithm determines the network graph, while standard approaches estimate the model parameters from data. Yet, with few data the corresponding assessments can be unreliable. To gain robustness in this phase, we consider a likelihood-based learning approach, which takes all the model quantifications whose likelihood exceeds a given threshold. A new classification algorithm based on this approach is presented. Notably, this is a credal classifier, i.e., more than a single class can be returned in output. This is the case when the Bayesian networks consistent with the threshold constraint assign different class labels to a test instance. This is the first classifier of this kind for general topologies. Experiments show how this approach provide the desired robustness.
-