%0 Conference Paper %1 ZhengWebbTing99 %A Zheng, Z. %A Webb, G. I. %A Ting, K. M. %B Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99) %C San Francisco %D 1999 %E Bratko, I. %E Dzeroski, S. %I Morgan Kaufmann %K Bayesian Conditional Estimation, Lazy Learning Learning, Probability Rules, %P 493-502 %T Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees %X LBR is a lazy semi-naive Bayesian classifier learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classification. To classify a test example, it creates a conjunctive rule that selects a most appropriate subset of training examples and induces a local naive Bayesian classifier using this subset. LBR can significantly improve the performance of the naive Bayesian classifier. A bias and variance analysis of LBR reveals that it significantly reduces the bias of naive Bayesian classification at a cost of a slight increase in variance. It is interesting to compare this lazy technique with boosting and bagging, two well-known state-of-the-art non-lazy learning techniques. Empirical comparison of LBR with boosting decision trees on discrete valued data shows that LBR has, on average, significantly lower variance and higher bias. As a result of the interaction of these effects, the average prediction error of LBR over a range of learning tasks is at a level directly comparable to boosting. LBR provides a very competitive discrete valued learning technique where error minimization is the primary concern. It is very efficient when a single classifier is to be applied to classify few cases, such as in a typical incremental learning scenario.

@inproceedings{ZhengWebbTing99, abstract = {LBR is a lazy semi-naive Bayesian classifier learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classification. To classify a test example, it creates a conjunctive rule that selects a most appropriate subset of training examples and induces a local naive Bayesian classifier using this subset. LBR can significantly improve the performance of the naive Bayesian classifier. A bias and variance analysis of LBR reveals that it significantly reduces the bias of naive Bayesian classification at a cost of a slight increase in variance. It is interesting to compare this lazy technique with boosting and bagging, two well-known state-of-the-art non-lazy learning techniques. Empirical comparison of LBR with boosting decision trees on discrete valued data shows that LBR has, on average, significantly lower variance and higher bias. As a result of the interaction of these effects, the average prediction error of LBR over a range of learning tasks is at a level directly comparable to boosting. LBR provides a very competitive discrete valued learning technique where error minimization is the primary concern. It is very efficient when a single classifier is to be applied to classify few cases, such as in a typical incremental learning scenario.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {San Francisco}, audit-trail = {Link via Citeseer}, author = {Zheng, Z. and Webb, G. I. and Ting, K. M.}, biburl = {https://www.bibsonomy.org/bibtex/26aba41235ba3537f2021f6795f729125/giwebb}, booktitle = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99)}, editor = {Bratko, I. and Dzeroski, S.}, interhash = {e8d1046ce533a8bc0b8f7d936f635cff}, intrahash = {6aba41235ba3537f2021f6795f729125}, keywords = {Bayesian Conditional Estimation, Lazy Learning Learning, Probability Rules,}, location = {Bled, Slovenia}, pages = {493-502}, publisher = {Morgan Kaufmann}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees}, year = 1999 }