Locally weighted naive Bayes

Frank, Eibe; Hall, Mark A.; Pfahringer, Bernhard

Locally weighted naive Bayes

dc.contributor.author	Frank, Eibe
dc.contributor.author	Hall, Mark A.
dc.contributor.author	Pfahringer, Bernhard
dc.date.accessioned	2008-10-09T03:56:52Z
dc.date.available	2008-10-09T03:56:52Z
dc.date.issued	2003-04
dc.description.abstract	Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes' primary weakness—attribute independence—and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxes the independence assumption by learning local models at prediction time. Experimental results show that locally weighted naive Bayes rarely degrades accuracy compared to standard naive Bayes and, in many cases, improves accuracy dramatically. The main advantage of this method compared to other techniques for enhancing naive Bayes is its conceptual and computational simplicity.	en_US
dc.format.mimetype	application/pdf
dc.identifier.citation	Frank, E., Hall, M. & Pfahringer, B. (2003). Locally weighted naive Bayes. (Working paper 04/03). Hamilton, New Zealand: University of Waikato, Department of Computer Science.	en_US
dc.identifier.issn	1170-487X
dc.identifier.uri	https://hdl.handle.net/10289/1008
dc.language.iso	en
dc.publisher	University of Waikato, Department of Computer Science	en_US
dc.relation.ispartofseries	Computer Science Working Papers
dc.subject	computer science	en_US
dc.subject	Machine learning
dc.title	Locally weighted naive Bayes	en_US
dc.type	Working Paper	en_US
uow.relation.series	04/03