@inproceedings{de199b8507d7478bbb6b266a36245dd2,
title = "Towards the optimal feature selection in high-dimensional Bayesian network classifiers",
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",
author = "Tatjana Pavlenko and Mikael Hall and \{von Rosen\}, Dietrich and Zhanna Andrushchenko",
year = "2004",
doi = "10.1007/978-3-540-44465-7\_76",
language = "English",
series = "Soft Methodology and Random Information Systems",
pages = "613--620",
booktitle = "2nd International Conference on Soft Methods in Probability and Statistics (SMPS 2004)",
publisher = "Springer, Berlin",
}