Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data

Combining the predictions of a set of classifiers has shown to be an effect ive way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predict ions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian n etwork structure is learned using a generic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Isla nds University Hospital. The accuracy obtained using the presented new appr oach statistically improve those obtained using standard machine learning m ethods. (C) 2001 Elsevier Science B.V. All rights reserved.