Floating search algorithm for structure learning of Bayesian network classifiers

This paper presents a floating search approach for learning the network structure of Bayesian network classifiers. A Bayesian network classifier is used which in combination with the search algorithm allows simultaneous feature selection and determination of the structure of the classifier. The introduced search algorithm enables conditional exclusions of previously added attributes and/or arcs from the network classifier. Hence, this algorithm is able to correct the network structure by removing attributes and/or arcs between the nodes if they become superfluous at a later stage of the search. Classification results of selective unrestricted Bayesian network classifiers are compared to na€ıve Bayes classifiers and tree augmented na€ıve Bayes classifiers. Experiments on different data sets show that selective unrestricted Bayesian network classifiers achieve a better classification accuracy estimate in two domains compared to tree augmented na€ıve Bayes classifiers, whereby in the remaining domains the performance is similar. However, the achieved network structure of selective unrestricted Bayesian network classifiers is simpler. 2003 Published by Elsevier B.V.