Text categorization for multi-label documents and many categories

Proceedings - IEEE Symposium on Computer-Based Medical Systems

Volumn , Issue , 2007, Pages 421-426

  Popa, I Sandu ^a   Zeitouni, K ^a   Gardarin, G ^a   Nakache, D ^b   Metais E ^b  

^a UNIVERSITY OF VERSAILLES   (France)

^b Conservatoire national des arts et métiers   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MEDICAL APPLICATIONS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

MULTI-LABEL DOCUMENTS; TEXT CATEGORIZATION; TEXTUAL DOCUMENTS;

CLASSIFICATION (OF INFORMATION);

EID: 34748870078     PISSN: 10637125     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CBMS.2007.108     Document Type: Conference Paper

References (20)

1. Mitchell, T., Machine Learning. Boston, MA, McGraw-Hill. 1998.
2. Yang, Y. and X. Liu, A re-examination of text categorization methods, in SIGIR-99. 1999.
3. Yang, Y. An evaluation of statistical approaches for text categorization, in Information Retrieval, 1(1). 1999.
4. Joachims, T., Text categorization with support vector machines: Learning with many relevant features. 1998.
5. Yang, Y., A scalability analysis of classifiers in text categorization, in Proceedings of SIGIR-03, 26th ACM International Conference on Research and Development in Information Retrieval, ACM Press, New York, US: Toronto, CA. 2003.
6. Cortes, C. and V. Vapnik, Support Vector Networks. 1995. p. 273-297.
7. W., E., J.O. Pedersen, and A.S. Weigend, A Neural Network Approach to Topic Spotting. 1993. p. 22-34.
8. Lewis, D.D. and, and M. Ringuette, A Comparison of two learning algorithms for text categorization. 1994. p. 81-93.
9. Rocchio, J.J., Relevance feedback in information retrieval, In the SMART retrieval system: Experiments in automatic document processing, Englewod Cliffs, NJ. 1971. p. 313-323.
10. Han, E.-H. and G. Karypis, Centroid-Based Document Classification: Analysis & Experimental Results, in Tech. Rep. 00-017, Computer Science, University of Minnesota. 2000.
11. Joachims, T., A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization, in Technical Report CMU-CS-96-118, Carnegie Mellon University. 1996.
12. Koller, D. and M. Sahami, Hierarchically classifying documents using very few words, in In Proceedings of the 14th International Conference on Machine Learning, Nashville, Tennessee. July 1997.
13. Kwok, J.T. -Y., Automated Text Categorization UsingSupport Vector Machine. 1998. p. 347-351.
14. Kou, H., Intelligent Web Wrapper Generation Using Text Mining Techniques. 2003, PhD Thesis, University of Versailles Saint-Quentin-en- Yvelines.
15. Official ICD10 Swiss Website, [cited; Available from: http://www.icd10.ch/index.asp
16. SMV light package. [cited; Available from: http://svmlight. joachims.org/.
17. Yang, Y. and J.O. Pedersen, A Comparative Study on Feature Selection in Text Categories, in Proceedings of the 14th International Conference on Machine Learning. 1997.
18. Joachims, T.: "Text categorization with support vector machines: learning with many relevant features". In C. Nédellec and C. Rouveirol, editors, Proceedings of ECML-98, 10th European Conference on Machine Learning, pages 137-142, Chemnitz, DE, 1998. Springer Verlag, Heidelberg, DE. Published in the "Lecture Notes in Computer Science" series, number 1398.
19. Boaz Lerner, Neil D. Lawrence : "A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics". Computer Laboratory, University of Cambridge, Cambridge, UK. Neural Comput & Applic (2001). 2001 Springer-Verlag London Limited.
20. K-M. Schneider : "On Word Frequency and Negative Evidence in Naive Bayes Text Classification", in proceedings of ESTAL2004, Adavances in Natural Language Processing, Alicante, Spain, LNAI Lecture Notes in Artificial Intelligence, Springer Verlag 2004, Volume 3230, pp 474-485.