Publication

Title of proceedings	Proceedings ISIPTA 2011
Place	Innsbruck, Austria

Bayesian networks are commonly used for classification: a structural learning algo- rithm determines the network graph, while standard approaches estimate the model parameters from data. Yet, with few data the corresponding assessments can be un- reliable. 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.