Web-Scale Classification with Naive Bayes

Presented at: 18th International World Wide Web Conference (WWW2009)


Webpage: http://www2009.eprints.org/122/1/p1083.pdf

Traditional Naive Bayes Classifier performs miserably on web-scale taxonomies. In this paper, we investigate the reasons behind such bad performance. We discover that the low performance are not completely caused by the intrinsic limitations of Naive Bayes, but mainly comes from two largely ignored problems: contradiction pair problem and discriminative evidence cancelation problem. We propose modifications that can alleviate the two problems while preserving the advantages of Naive Bayes. The experimental results show our modified Naive Bayes can significantly improve the performance on real web-scale taxonomies.

Keywords: Poster Session

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/122

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