Web-scale classification with naive bayes

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Web-scale classification with naive bayes

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International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents

Madrid, Spain

POSTER SESSION: Wednesday, April 22, 2009 table of contents

Pages 1083-1084

Year of Publication: 2009

ISBN:978-1-60558-487-4

Authors

Congle Zhang	Shanghai Jiao Tong University, Shanghai, China
Gui-Rong Xue	Shanghai Jiao Tong University, Shanghai, China
Yong Yu	Shanghai Jiao Tong University, Shanghai, China
Hongyuan Zha	College of Computing Georgia Institute of Technology, Atlanta, USA

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ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Lijuan Cai , Thomas Hofmann, Hierarchical document categorization with support vector machines, Proceedings of the thirteenth ACM international conference on Information and knowledge management, November 08-13, 2004, Washington, D.C., USA [doi>10.1145/1031171.1031186]
	2	J. Rennie, L. Shih, J. Teevan, and D. Karger. Tackling the poor assumptions of naive bayes text classifiers. In ICML 2003, pages 285--295, 2003.
	3	Gui-Rong Xue , Dikan Xing , Qiang Yang , Yong Yu, Deep classification in large-scale text hierarchies, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390440]