An experimental study on large-scale web categorization

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An experimental study on large-scale web categorization

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International World Wide Web Conference archive
Special interest tracks and posters of the 14th international conference on World Wide Web table of contents

Chiba, Japan

POSTER SESSION: Posters table of contents

Pages: 1106 - 1107

Year of Publication: 2005

ISBN:1-59593-051-5

Authors

Tie-Yan LIU	Microsoft Research Asia, Beijing, P. R. China
Yiming YANG	Carnegie Mellon University, PA
Hao WAN	Tsinghua University, Beijing, P. R. China
Qian ZHOU	Tsinghua University, Beijing, P. R. China
Bin GAO	Peking University, Beijing, P. R. China
Hua-Jun ZENG	Microsoft Research Asia, Beijing, P. R. China
Zheng CHEN	Microsoft Research Asia, Beijing, P. R. China
Wei-Ying MA	Microsoft Research Asia, Beijing, P. R. China

Sponsor

ACM: Association for Computing Machinery

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ACM New York, NY, USA

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Downloads (6 Weeks): 7, Downloads (12 Months): 51, Citation Count: 4

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ABSTRACT

Taxonomies of the Web typically have hundreds of thousands of categories and skewed category distribution over documents. It is not clear whether existing text classification technologies can perform well on and scale up to such large-scale applications. To understand this, we conducted the evaluation of several representative methods (Support Vector Machines, k-Nearest Neighbor and Naive Bayes) with Yahoo! taxonomies. In particular, we evaluated the effectiveness/efficiency tradeoff in classifiers with hierarchical setting compared to conventional (flat) setting, and tested popular threshold tuning strategies for their scalability and accuracy in large-scale classification problems.

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.

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	2	George Forman, An extensive empirical study of feature selection metrics for text classification, The Journal of Machine Learning Research, 3, 3/1/2003
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	7	Yiming Yang, A study of thresholding strategies for text categorization, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.137-145, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383975]

CITED BY 4

	Bin Gao , Tie-Yan Liu , Guang Feng , Tao Qin , Qian-Sheng Cheng , Wei-Ying Ma, Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Copartitioning, IEEE Transactions on Knowledge and Data Engineering, v.17 n.9, p.1263-1273, September 2005
	Steven C. H. Hoi , Rong Jin , Michael R. Lyu, Large-scale text categorization by batch mode active learning, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland
	Rafal Rak , Lukasz Kurgan , Marek Reformat, A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation, Data & Knowledge Engineering, v.64 n.1, p.171-197, January, 2008
	Lei Tang , Suju Rajan , Vijay K. Narayanan, Large scale multi-label classification via metalabeler, Proceedings of the 18th international conference on World wide web, April 20-24, 2009, Madrid, Spain