%0 Conference Paper %1 WangWebb02b %A Wang, Z. %A Webb, G. I. %B Proceedings of the First Australasian Data Mining Workshop (AusDM02) %C Sydney %D 2002 %E Simoff, S.J %E Williams, G.J %E Hegland, M. %I University of Technology %K Conditional Estimation Probability %P 57-63 %T A Heuristic Lazy Bayesian Rules Algorithm %X Lazy Bayesian rule has demonstrated outstanding classification accuracy. However, it has high computational overheads when large numbers of instances are classified from a single training set. We compare lazy Bayesian rule and the tree-augmented Bayesian classifier, and present a new heuristic lazy Bayesian rule classifier that combines elements of the two. It requires less computation than lazy Bayesian rule, but demonstrates similar prediction accuracy.

@inproceedings{WangWebb02b, abstract = {Lazy Bayesian rule has demonstrated outstanding classification accuracy. However, it has high computational overheads when large numbers of instances are classified from a single training set. We compare lazy Bayesian rule and the tree-augmented Bayesian classifier, and present a new heuristic lazy Bayesian rule classifier that combines elements of the two. It requires less computation than lazy Bayesian rule, but demonstrates similar prediction accuracy.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Sydney}, audit-trail = {*}, author = {Wang, Z. and Webb, G. I.}, biburl = {https://www.bibsonomy.org/bibtex/26dcd3a55b4e2fa203171f00acc5693cb/giwebb}, booktitle = {Proceedings of the First Australasian Data Mining Workshop (AusDM02)}, editor = {Simoff, S.J and Williams, G.J and Hegland, M.}, interhash = {a045385547be202863b212a422f6281e}, intrahash = {6dcd3a55b4e2fa203171f00acc5693cb}, keywords = {Conditional Estimation Probability}, location = {Canberra, Australia}, pages = {57-63}, publisher = {University of Technology}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {A Heuristic Lazy Bayesian Rules Algorithm}, year = 2002 }