Please use this identifier to cite or link to this item:
|Title:||A comprehensive comparative study on term weighting schemes for text categorization with support vector machines|
|Authors:||Lan, M. |
Term weighting schemes
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2018
checked on Dec 8, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.