Document indexing in text categorization Export

@inproceedings{citeulike:1118960, abstract = {Aiming at the characteristic of text categorization, this paper proposes an improved method of computing term weights, tfidfie, based on the traditional tfidf function that is generally used in most classifiers. In comparison with the tfidf function, the tfidfie function adds an information entropy factor, H, which represents the distribution of documents in the training set in which the term occurs. The experiments show tfidfie outperforms tfidf. In addition, this paper analyses the difference of using information entropy factor H between document categorization and feature selection, also finds that both two phases are all necessary for text categorization, meanwhile it can reach the best performance of classification with up to 70\% of the unique terms being removed.}, author = {Zhang, Qi-Rui and Zhang, Ling and Dong, Shou-Bin and Tan, Jing-Hua}, citeulike-article-id = {1118960}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1527600}, journal = {Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on}, keywords = {document, indexing, mining, text}, pages = {3792--3796 Vol. 6}, posted-at = {2007-02-23 14:19:14}, priority = {3}, title = {Document indexing in text categorization}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1527600}, volume = {6}, year = {2005} }

TY - CONF ID - citeulike:1118960 L3 - citeulike-article-id:1118960 N2 - Aiming at the characteristic of text categorization, this paper proposes an improved method of computing term weights, tfidfie, based on the traditional tfidf function that is generally used in most classifiers. In comparison with the tfidf function, the tfidfie function adds an information entropy factor, H, which represents the distribution of documents in the training set in which the term occurs. The experiments show tfidfie outperforms tfidf. In addition, this paper analyses the difference of using information entropy factor H between document categorization and feature selection, also finds that both two phases are all necessary for text categorization, meanwhile it can reach the best performance of classification with up to 70% of the unique terms being removed. JF - Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on EP - 3796 Vol. 6 TI - Document indexing in text categorization VL - 6 SP - 3792 KW - document KW - indexing KW - mining KW - text AU - Zhang, Qi-Rui AU - Zhang, Ling AU - Dong, Shou-Bin AU - Tan, Jing-Hua PY - 2005/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1527600 ER -