An experimental study on large-scale web categorization

Authors:
Tie-Yan LIU;Yiming YANG;Hao WAN;Qian ZHOU;Bin GAO;Hua-Jun ZENG;Zheng CHEN;Wei-Ying MA
Affiliations:
Microsoft Research Asia, Beijing, P. R. China;Carnegie Mellon University, PA;Tsinghua University, Beijing, P. R. China;Tsinghua University, Beijing, P. R. China;Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

Citing 5
Cited 6

A re-examination of text categorization methods

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of thresholding strategies for text categorization

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization

ACM Computing Surveys (CSUR)
An extensive empirical study of feature selection metrics for text classification

The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research

The Journal of Machine Learning Research

Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Copartitioning

IEEE Transactions on Knowledge and Data Engineering
Large-scale text categorization by batch mode active learning

Proceedings of the 15th international conference on World Wide Web
A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation

Data & Knowledge Engineering
Large scale multi-label classification via metalabeler

Proceedings of the 18th international conference on World wide web
The ECIR 2010 large scale hierarchical classification workshop

ACM SIGIR Forum
Time-weighted web authoritative ranking

Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

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.