How weak categorizers based upon different principles strengthen performance

Computer Journal

Volumn 45, Issue 5, 2002, Pages 511-524

  Uren, Victoria S ^a   Addis, Thomas R ^b  

^a OPEN UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SOFTWARE; ERROR ANALYSIS; MATHEMATICAL MODELS; PROBABILITY; TEXT PROCESSING;

TEXT CLASSIFIERS;

LEARNING SYSTEMS;

EID: 0036394159     PISSN: 00104620     EISSN: None     Source Type: Journal    
DOI: 10.1093/comjnl/45.5.511     Document Type: Article

References (33)

1. Quinlan, J.R. (1996) Bagging, Boosting and C4.5. In Proc. 13th Int. Conf. on Artificial Intelligence (AAAI96), Portland, OR, 4-8 August, pp. 725-730. AAAI Press/The MIT Press, Cambridge, MA.
2. Opitz, D. and Maclin, R. (1999) Popular ensemble methods: an empirical study. J. Artificial Intell. Res., 11, 169-198.
3. Breiman, L. (1996) Bagging predictors. Machine Learning, 24, 123-140.
4. Hull, D.A., Pedersen, J.O. and Schützte, H. (1996) Method combination for document filtering. In SIGIR 96. Proc. 19th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Zurich, Switzerland, 18-22 August, pp. 279-287. Hartung-Gorre, Konstanz.
5. Larkey, L.S. and Croft, W.B. (1996) Combining classifiers in text categorization. In SIGIR 96. Proc. 19th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Zurich, Switzerland, 18-22 August, pp. 289-297. Hartung-Gorre, Konstanz.
6. Li, Y.H. and Jain, A.K. (1998) Classification of text documents. Comp. J., 41(8), 537-546.
7. Scott, S. and Matwin, S. (1999) Feature engineering for text classification, In Machine Learning, Proc. 16th Int. Conf. (ICML'99), Bled, Slovenia, 27-30 June, pp. 379-388. Morgan Kaufmann, San Francisco, CA.
8. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K. and Slattery, S. (2000) Learning to construct knowledge bases from the World Wide Web. Artificial Intell., 118, 69-113.
9. Sebastiani, F. (1999) A tutorial on automated text categorisation. In Proc. ASAI-99, 1st Argentinian Symposium on Artificial Intell., Buenos Aires, 7-10 September, pp. 7-35.
10. Wiebe, J., Hirst, G. and Horton, D. (1996) Language use in context. Commun. ACM, 39(1), 102-111.
11. Tumer, K. and Ghosh, J. (1999) Linear and order statistics combiners for pattern classification. Combining Artificial Neural Networks, pp. 127-162. Springer, New York.
12. Eckhardt, D.E. and Lee, L.D. (1985) A theoretical basis for the analysis of multiversion software subject to coincident errors. IEEE Trans. Software Eng., 11, 1511-1517.
13. Lewis, D.D. (1995) Evaluating and optimizing autonomous text classification systems. In Proc. 18th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Seattle, WA, 9-13 July, pp. 246-254. ACM Press, New York.
14. Hansen, L.K. and Salamon, P. (1990) Neural network ensembles. IEEE Trans. Pattern Analysis Machine Intell., 12(19), 993-1001.
15. Mitchell, T.M. (1997) Machine Learning. McGraw-Hill, New York.
16. Fuhr, N. and Buckley, C. (1991) A probabilistic learning approach for document indexing. ACM Trans. Inform. Syst., 9(3), 223-248.
17. Cohen, W.W. and Singer, Y. (1996) Context-sensitive learning methods for text categorization. In Proc. 19th Annual Int. ACM-SIGIR Conf. on Research and Development in Information Retrieval. Zurich, Switzerland, 18-22 August, pp. 307-316. Hartung-Gorre, Konstanz.
18. Finch, S. (1995) Partial orders for document representation: a new methodology for combining document features. In Proc. 18th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Seattle, WA, 9-13 July, pp. 264-72. ACM Press, New York.
19. Junker, M. and Abecker, A. (1997) Exploiting thesaurus knowledge in rule induction for text classification. In Proc. RANLP-97 2nd Int. Conf. on Recent Advances in NLP, Tzigov Chark, Bulgaria, 11-13 September, pp. 202-207.
20. Yang, Y. (1999) An evaluation of statistical approaches to text categorization. Inform. Retrieval, 1(1), 69-90.
21. Hull, D. (1993) Using statistical testing in the evaluation of retrieval experiments. In SIGIR 93. Proc. 16th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Pittsburgh, PA, 27 June-1 July, pp. 329-338. ACM, New York.
22. Lewis, D.D. (1992) An evaluation of phrasal and clustered representations on a text categorization task. In Proc. 15th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Copenhagen, Denmark, 21-24 June, pp. 37-50. ACM, New York.
23. Lynch, M.F., Petrie, J.H. and Snell, M.J. (1973) Analysis of the microstructure of titles in the Inspec data-base. Inform. Storage Retrieval, 9, 331-337.
24. Robertson, A.M. and Willett, P. (1998) Applications of N-grams in textual information systems. J. Documentation, 54(1), 48-69.
25. Miller, E., Shen, D., Liu, J. and Nicholas, C. (2000) Performance and scalability of a large-scale N-gram based information retrieval system. J. Digital Inform., 1(5) http://jodi.ecs.soton.ac.uk/.
26. Ekmekçioglu, F.C., Lynch M.F., Robertson, A.M., Sembok, T.M.T. and Willett, P. (1996) Comparison of n-gram matching and stemming for term conflation in English, Malay and Turkish texts. Text Technol., 6(1), pp. 1-14.
27. Cavnar, W.B. and Trenkle, J.M. (1994) N-gram-based text categorization. In Proc. 3rd Annual Symp. on Document Analysis and Information Retrieval (SDAIR-94), Las Vegas, NV, 11-13 April, pp. 161-175. UNLV Publications/Reprographics, Las Vegas, NV.
28. Damashek, M. (1995) Ganging similarity with n-grams: language-independent categorization of text. Science, 267(10), pp. 843-848.
29. Huffman, S. (1996) Acquaintance: language-independent document categorization by N-grams. The Fourth Text Retrieval Conference (TREC-4), Gaithersburg, Maryland, 1-3 November, pp. 359-371. NIST, Gaithersburg, MD.
30. Uren, V.S. (2000) An evaluation of text categorisation errors. In Proc. Workshop on the Evaluation of Information Management Systems, London, 15 September, pp. 79-87. Queen Mary and Westfield College, London.
31. Lewis, D.D. (1992) Feature selection and feature extraction for text categorization. In Proc. Speech and Natural Language Workshop, February, pp. 212-217. DARPA/Morgan Kaufmann, San Mateo.
32. Shannon, C.E. and Weaver W. (1949) The Mathematical Theory of Communication, University of Illinois Press, Urbana, IL.
33. Apte, C., Damerau, F. and Weiss, S.M. (1994) Automated learning of decision rules for text categorisation. ACM Trans. Inform. Syst., 12(3), 233-251.