von Rosen, Dietrich
- Department of Energy and Technology, Swedish University of Agricultural Sciences
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
SLU Authors
Andrushchenko, Zhanna
- Department of Energy and Technology, Swedish University of Agricultural Sciences
- Department of Energy and Technology, Swedish University of Agricultural Sciences
Permanent link to this page (URI)
https://res.slu.se/id/publ/7268