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Likelihood-Based Naive Credal Classifier

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Antonucci Alessandro, Cattaneo Marco E. G. V., Corani Giorgio,
Project Multi Model Inference for dealing with uncertainty in environmental models
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Proceedings (peer-reviewed)

Title of proceedings Proceedings ISIPTA 2011
Place Innsbruck, Austria

Abstract

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.
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