Abstract

We focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the growing dimension asymptotics. We modify the weighted BN by introducing inclusion-exclusion factors which eliminate the features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy

Published in

Soft Methodology and Random Information Systems
2004, pages: 613-620
Publisher: Springer, Berlin

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