Document transformation for multi-label feature selection in text categorization

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2007, Pages 451-456

  Chen, Weizhu ^a   Yan, Jun ^a   Zhang, Benyu ^a   Chen, Zheng ^a   Yang, Qiang ^b  

^a MICROSOFT RESEARCH ASIA   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; DATA MINING; DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; LABELS; MATHEMATICAL TRANSFORMATIONS; MINING; SEARCH ENGINES; STANDARDS; TEXT PROCESSING; VIDEO STREAMING;

AUTOMATIC TEXT CATEGORIZATION; COMPARATIVE STUDIES; DOCUMENT TRANSFORMATION; FEATURE SELECTION; FEATURE SELECTION ALGORITHMS; INTERNATIONAL CONFERENCES; MULTI-CLASS CATEGORIZATION; RESEARCH AREA; SVM CLASSIFIERS; TEXT CATEGORIZATION;

LABELING;

EID: 49749095082     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2007.18     Document Type: Conference Paper

References (14)

1. A. McCallum. Multi-label text classification with a mixture model trained by EM. AAAI99 work shop on Text Learning.
2. A.McCallum and N. Ghamrawi. Colletive multi-label text classification. In Proceedings of CIKM, pages 195-200, Bremen, Genmany, 2005.
3. Brianca Zadrozny, John Landford, and Naoki Abe. Cost-sensitive learning by cost-proportionate example weighting. In Proceeding of the ICDM 2003.
4. Debole, F. and Sebastiani, F. An analysis of the relative hardness of Reuters-21578 subsets. Journal of the American Society for Information Science and Technology, 56 (6). 584-596
5. http://en.wikipedia.org/wiki/Entropy.
6. http://svmlight.joachims.org/
7. Lewis, D.D., Yang, Y., Rose, T.G. and Li, F. RCV1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research, 5. 361-397.
8. M.Boutell, X.Shen, J.Luo, and C.Brown. Multi-label semantic scene classification, technical report, dept.comp.sci.u.rochester.2003
9. P.Kantor and J.Lee. The maximum entropy principle in information retrieval. In proceeding of the ACM SIGIR Conference, 1986.
10. Salton G. and Buckley C., "Term weighting approaches in automatic text retrieval", Information Processing and Management, vol. 24, pp. 513-523, 1988.
11. Yan, J., Liu, N., Zhang, B., Yan, S., Chen, Z., Cheng, Q., Fan, W. and Ma, W.-Y., OCFS: optimal orthogonal centroid feature selection for text categorization. In Proceedings of ACM SIGIR Conference, Salvador, Brazil, 2005, 122-129
12. Yang, Y. and Pedersen, J.O., A comparative Study on Feature Selection in Text Categorization. In Proceedings of the 14th ICML, (1997), 412-420.
13. Yang, Y and Liu .X, A re-examination of text categorization methods. Proceedings of ACM SIGIR Conference (SIGIR'99, pp 42-9), 1999.
14. Yu, K., Yu, S. and Tresp, V., Multi-label Informed Latent Semantic Indexing. In Proceedings of t ACM SIGIR, (Salvador, Brazil, 2005), 258-265.