Please use this identifier to cite or link to this item: https://doi.org/10.1145/1062745.1062854
Title: A comprehensive comparative study on term weighting schemes for text categorization with support vector machines
Authors: Lan, M. 
Tan, C.-L. 
Low, H.-B.
Sung, S.-Y. 
Keywords: Categorization
SVM
Term weighting schemes
Text
Issue Date: 2005
Source: Lan, M.,Tan, C.-L.,Low, H.-B.,Sung, S.-Y. (2005). A comprehensive comparative study on term weighting schemes for text categorization with support vector machines. 14th International World Wide Web Conference, WWW2005 : 1032-1033. ScholarBank@NUS Repository. https://doi.org/10.1145/1062745.1062854
Abstract: Term weighting scheme, which has been used to convert the documents as vectors in the term space, is a vital step in automatic text categorization. In this paper, we conducted comprehensive experiments to compare various term weighting schemes with SVM on two widely-used benchmark data sets. We also presented a new term weighting scheme tf-rf to improve the term's discriminating power. The controlled experimental results showed that this newly proposed tf-rf scheme is significantly better than other widely-used term weighting schemes. Compared with schemes related with tf factor alone, the idf factor does not improve or even decrease the term's discriminating power for text categorization.
Source Title: 14th International World Wide Web Conference, WWW2005
URI: http://scholarbank.nus.edu.sg/handle/10635/40913
ISBN: 1595930515
DOI: 10.1145/1062745.1062854
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