Learning Bayesian Networks Using Feature Selection

Gregory M. Provan, Moninder Singh
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:450-456, 1995.

Abstract

This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features which maximizes predictive accuracy prior to the network learning phase. We examine explicitly the effects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks which are computationally simpler to evaluate and which display predictive accuracy comparable to that of Bayesian networks which model all attributes.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-provan95a, title = {Learning Bayesian Networks Using Feature Selection}, author = {Provan, Gregory M. and Singh, Moninder}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {450--456}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/provan95a/provan95a.pdf}, url = {https://proceedings.mlr.press/r0/provan95a.html}, abstract = {This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features which maximizes predictive accuracy prior to the network learning phase. We examine explicitly the effects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks which are computationally simpler to evaluate and which display predictive accuracy comparable to that of Bayesian networks which model all attributes.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Learning Bayesian Networks Using Feature Selection %A Gregory M. Provan %A Moninder Singh %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-provan95a %I PMLR %P 450--456 %U https://proceedings.mlr.press/r0/provan95a.html %V R0 %X This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features which maximizes predictive accuracy prior to the network learning phase. We examine explicitly the effects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks which are computationally simpler to evaluate and which display predictive accuracy comparable to that of Bayesian networks which model all attributes. %Z Reissued by PMLR on 01 May 2022.
APA
Provan, G.M. & Singh, M.. (1995). Learning Bayesian Networks Using Feature Selection. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:450-456 Available from https://proceedings.mlr.press/r0/provan95a.html. Reissued by PMLR on 01 May 2022.

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