Inferring, Learning and Modelling Complex Systems with Bayesian Networks. A Tutorial

Abstract

Bayesian networks, BN, are a formalism for probabilistic reasoning that have grown increasingly popular for tasks such as classification in data-mining. In some situations, the structure of the Bayesian network can be given by an expert. If not, retrieving it automatically from a database of cases is a NP-hard problem; notably because of the complexity of the search space. In the last decade, numerous methods have been introduced to learn the networks structure automatically, by simplifying the search space or by using a heuristic in the search space. Most methods deal with completely observed data, but some can deal with incomplete data. In this tutorial we will present, besides BN, other popular classification methods, i.e. Multilayer Perceptrons Network (MLP) and K-nearest neighbor (KNN) an analyze their performance in the context of medical diagnosis.