H. Shi, Z. Wang, G. Webb, and H. Huang. Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03), page 265-270. Berlin/Heidelberg, Springer-Verlag, (2003)
Abstract
On the basis of examining the existing restricted Bayesian network classifiers, a new Bayes-theorem-based and more strictly restricted Bayesian-network-based classification model DLBAN is proposed, which can be viewed as a double-level Bayesian network augmented naive Bayes classification. The experimental results show that the DLBAN classifier is better than the TAN classifier in the most cases.
Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03)
%0 Conference Paper
%1 ShiWangWebbHuang03
%A Shi, H.
%A Wang, Z.
%A Webb, G.I.
%A Huang, H.
%B Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03)
%C Berlin/Heidelberg
%D 2003
%E Whang, K-Y.
%E Jeon, J.
%E Shim, K.
%E Srivastava, J.
%I Springer-Verlag
%K Conditional Estimation Probability
%P 265-270
%T A New Restricted Bayesian Network Classifier
%X On the basis of examining the existing restricted Bayesian network classifiers, a new Bayes-theorem-based and more strictly restricted Bayesian-network-based classification model DLBAN is proposed, which can be viewed as a double-level Bayesian network augmented naive Bayes classification. The experimental results show that the DLBAN classifier is better than the TAN classifier in the most cases.
@inproceedings{ShiWangWebbHuang03,
abstract = {On the basis of examining the existing restricted Bayesian network classifiers, a new Bayes-theorem-based and more strictly restricted Bayesian-network-based classification model DLBAN is proposed, which can be viewed as a double-level Bayesian network augmented naive Bayes classification. The experimental results show that the DLBAN classifier is better than the TAN classifier in the most cases.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {Berlin/Heidelberg},
audit-trail = {*},
author = {Shi, H. and Wang, Z. and Webb, G.I. and Huang, H.},
biburl = {https://www.bibsonomy.org/bibtex/235003efa7ebb94b53d11d68db05d3097/giwebb},
booktitle = {Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh {Pacific}-{Asia} Conference on Knowledge Discovery and Data Mining (PAKDD'03)},
editor = {Whang, K-Y. and Jeon, J. and Shim, K. and Srivastava, J.},
interhash = {ebd9dee4a30e1cda27ccd05a178b70a0},
intrahash = {35003efa7ebb94b53d11d68db05d3097},
keywords = {Conditional Estimation Probability},
location = {Seoul, Korea},
pages = {265-270},
publisher = {Springer-Verlag},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {A New Restricted Bayesian Network Classifier},
year = 2003
}