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dc.contributor.authorKibriya, Ashraf Masood
dc.contributor.authorFrank, Eibe
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.coverage.spatialConference held at Cairns, Australiaen_NZ
dc.identifier.citationKibriya, A. M., Frank, E., Pfahringer, B. & Holmes, G. (2005). Multinomial naive Bayes for text categorization revisited. In G.I. Webb & Xinghuo Yu(Eds.), Proceedings of 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004.(pp. 488-499). Berlin: Springer.en_US
dc.description.abstractThis paper presents empirical results for several versions of the multinomial naive Bayes classifier on four text categorization problems, and a way of improving it using locally weighted learning. More specifically, it compares standard multinomial naive Bayes to the recently proposed transformed weight-normalized complement naive Bayes classifier (TWCNB) [1], and shows that some of the modifications included in TWCNB may not be necessary to achieve optimum performance on some datasets. However, it does show that TFIDF conversion and document length normalization are important. It also shows that support vector machines can, in fact, sometimes very significantly outperform both methods. Finally, it shows how the performance of multinomial naive Bayes can be improved using locally weighted learning. However, the overall conclusion of our paper is that support vector machines are still the method of choice if the aim is to maximize accuracy.en_US
dc.sourceAI 2004en_NZ
dc.subjectcomputer scienceen_US
dc.subjectmultinomial naive Bayes classifieren_US
dc.subjectMachine learning
dc.titleMultinomial naive Bayes for text categorization revisiteden_US
dc.typeConference Contributionen_US
dc.relation.isPartOfAdvances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligenceen_NZ
pubs.volumeLNAI 3339en_NZ

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