document: An experimental study on large-scale web categorization 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 .
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