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 effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions 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 network structure is learned using a genetic 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 Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.
Sierra
B
B
Department of Computer Science and Artificial Intelligence, University of the Basque Country, P.O. Box 649, E-20080, San Sebastián, Spain. ccpsiarb@si.ehu.es
Serrano
N
N
Larrañaga
P
P
Plasencia
E J
EJ
Inza
I
I
Jiménez
J J
JJ
Revuelta
P
P
Mora
M L
ML
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003663:Decision Trees; D005823:Genetics / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized; D010347:Patient Care Planning