Search-and-score structure learning algorithm for Bayesian network classifiers

Franz Pernkopf; Paul O'Leary

doi:10.1117/12.515041

1 May 2003 Search-and-score structure learning algorithm for Bayesian network classifiers

Franz Pernkopf, Paul O'Leary

Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.515041
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States

Abstract

This paper presents a search-and-score approach for determining the network structure of Bayesian network classifiers. A selective unrestricted 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 naive Bayes classifiers and tree augmented naive 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 naive Bayes classifiers, whereby in the remaining domains the performance is similar. However, the achieved network structure of selective unrestricted Bayesian network classifiers is simpler and computationally more efficient.

Citation Download Citation

Franz Pernkopf and Paul O'Leary "Search-and-score structure learning algorithm for Bayesian network classifiers", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); https://doi.org/10.1117/12.515041

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