%0 Conference Paper %1 ZhengWebb05 %A Zheng, F. %A Webb, G.I. %B Proceedings of the Fourth Australasian Data Mining Conference (AusDM05) %C Sydney %D 2005 %E Simoff, S.J. %E Williams, G.J. %E Galloway, J. %E Kolyshkina, I. %I University of Technology %K AODE, Conditional Estimation Probability %P 141-156 %T A Comparative Study of Semi-naive Bayes Methods in Classification Learning %X Numerous techniques have sought to improve the accuracy of Naive Bayes (NB) by alleviating the attribute interdependence problem. This paper summarizes these semi-naive Bayesian methods into two groups: those that apply conventional NB with a new attribute set, and those that alter NB by allowing inter-dependencies between attributes. We review eight typical semi-naive Bayesian learning algorithms and perform error analysis using the bias-variance decomposition on thirty-six natural domains from the UCI Machine Learning Repository. In analysing the results of these experiments we provide general recommendations for selection between methods.

@inproceedings{ZhengWebb05, abstract = {Numerous techniques have sought to improve the accuracy of Naive Bayes (NB) by alleviating the attribute interdependence problem. This paper summarizes these semi-naive Bayesian methods into two groups: those that apply conventional NB with a new attribute set, and those that alter NB by allowing inter-dependencies between attributes. We review eight typical semi-naive Bayesian learning algorithms and perform error analysis using the bias-variance decomposition on thirty-six natural domains from the UCI Machine Learning Repository. In analysing the results of these experiments we provide general recommendations for selection between methods.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Sydney}, author = {Zheng, F. and Webb, G.I.}, biburl = {https://www.bibsonomy.org/bibtex/295dbd2981112a8454dfbb85d748230f2/giwebb}, booktitle = {Proceedings of the Fourth Australasian Data Mining Conference (AusDM05)}, editor = {Simoff, S.J. and Williams, G.J. and Galloway, J. and Kolyshkina, I.}, interhash = {50b93b858e085f20bb4f031072ed09e2}, intrahash = {95dbd2981112a8454dfbb85d748230f2}, keywords = {AODE, Conditional Estimation Probability}, location = {Sydney, Australia}, pages = {141-156}, publisher = {University of Technology}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {A Comparative Study of Semi-naive Bayes Methods in Classification Learning}, year = 2005 }