Improving performance of text categorization by combining filtering and support vector machines

Journal of the American Society for Information Science and Technology

Volumn 55, Issue 7, 2004, Pages 579-592

  Díaz, Irene ^a   Ranilla, José ^a   Montañes, Elena ^a   Fernández, Javier ^a   Combarro, Elías F ^a  

^a UNIVERSITY OF OVIEDO   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATION; PROBLEM SOLVING; VECTORS; VOCABULARY CONTROL; WORD PROCESSING;

SUPPORT VECTOR MACHINES (SVM); TEXT CATEGORIZATION; TIME-CONSUMING TASK; WORD RELEVANCE;

INFORMATION RETRIEVAL SYSTEMS;

EID: 2342572969     PISSN: 15322882     EISSN: None     Source Type: Journal    
DOI: 10.1002/asi.10409     Document Type: Article

References (25)

1. Aas, K., & Eikvil, L. (1999). Text categorisation: A survey. Oslo, Norway: Norwegian Computing Center.
2. Apte, C., Damerau, F., & Weiss, S.M. (1994). Automated learning of decision rules for text categorization. Information Systems, 12(3), 233-251.
3. Colin, A. (1996). Building decision trees with the ID3 algorithm. Dr. Dobbs Journal.
4. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
5. Fernández, J., Combarro, E.F., Díaz, I., Montañés, E., & Ranilla, J. (2003). Text categorisation with support vector machines and feature reduction. In M. Mohammadian (Ed.), Intelligent Agents for Data Mining and Information Retrieval, Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation (CI-MCA'03). Idea Group Publishing.
6. Joachims, T. (1998a). Text categorization with support vector machines: Learning with many relevant features. Proceedings of the 10th European Conference on Machine Learning (ECML'98) (pp. 137-142), Heidelberg, Germany: Springer-Verlag.
7. Joachims, T. (1998b). Making large-scale support vector machine learning practical. In B. Scholkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods: Support vector machines. Cambridge, MA: MIT Press.
8. Koller, D., & Sahami, M. (1997). Hierarchically classifying documents using very few words. Proceedings of the 14th International Conference on Machine Learning (ICML'97) (pp. 170-178). San Francisco, CA: Morgan Kaufmann Publishers.
9. Kononenko, I. (1995). On biases estimating multi-valued attributes. Proceedings of the 14th International Conference on Artificial Intelligence (pp. 1034-1040). Montreal, Canada.
10. Lewis, D.D., & Ringuette, M. (1994). A comparison of two learning algorithms for text categorization. Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval (SDAIR'94) (pp. 81-93), Las Vegas, NV: University of Nevada.
11. Ohsumed 91 Collection. Retrieved March 23, 2002, from http://trec.nist.gov/data/t9-filtering
12. Porter, M.G. (1980). An algorithm for suffix stripping. Program, 45(3), 130-137.
13. Quinlan, J.R. (1993). Constructing decision trees in C4.5. Programs of Machine Learning, 17-26. San Mateo, CA.
14. Ranilla, J., & Bahamonde, A. (2002). Fan: Finding accurate inductions. International Journal of Human Computer Studies, 56(4), 445-474.
15. Reuters Collection. Retrieved March 23, 2002, from http://www.research. attp.com/lewis'/reuters21578.html
16. Rochio, J. (1971). Relevance feedback in information retrieval. In G. Salton (Ed.), the SMART retrieval system, experiments in automatic document processing (pp. 313-323). Englewood Cliffs, NJ: Prentice-Hall.
17. Salton, G., & McGill, M.J. (1983). An introduction to modern information retrieval. New York: McGraw-Hill.
18. Sebastiani, F. (2002). Machine learning in automated text categorisation. ACM Computing Surveys 34(1), 1-47.
19. light. Retrieved January 10, 2003, from http://svmlight.joachims.org
20. Van Rijsbergen, C.J. (1979). Information retrieval. London: Butterworths.
21. Van Rijsbergen, C.J., Harper, D.J., & Porter, M.F. (1981). The selection of good search terms. Information Processing and Management, 17, 77-91.
22. Vapnik, V. (1995). The nature of statistical learning theory. Berlin, Germany: Springer-Verlag.
23. Yang, T. (1994). Expert network: Effective and efficient learning from human decisions in text categorisation and retrieval. Proceedings of International Conference on Research and Development in Information Retrieval (SIGIR'94) (pp. 13-22). Dublin, Ireland: ACM.
24. Yang, T., & Pedersen, J.P. (1997a). Feature selection in statistical learning of text categorization. Proceedings of the 14th International Conference on Machine Learning (ICML'97) (pp. 412-420).
25. Yang, T., & Pedersen, J.P. (1997b). A comparative study on feature selection in text categorisation. Proceedings of the 14th International Conference on Machine Learning (ICML'97) (pp. 412-420). San Francisco, CA.