Probabilistic combination of text classifiers using reliability indicators: Models and results

SIGIR Forum (ACM Special Interest Group on Information Retrieval)

Volumn , Issue , 2002, Pages 207-214

  Bennett, Paul N ^a   Dumais, Susan T ^b   Horvitz, Eric ^b  

^a Carnegie Mellon University   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Classifier combination; Metaclassifiers; Reliability indicators; Text classification

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); INFORMATION RETRIEVAL; PERFORMANCE; SEARCH ENGINES;

CLASSIFIER COMBINATION; METACLASSIFIERS; PROBABILISTIC COMBINATION; RELIABILITY INDICATORS; TEXT CLASSIFICATION;

TEXT PROCESSING;

EID: 0036992555     PISSN: 01635840     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (31)

1. K. Al-Kofahi, A. Tyrrell, A. Vacher, T. Travers, and P. Jackson. Combining multiple classifiers for text categorization. In CIKM '01, pages 97-104, 2001.
2. B. T. Bartell, G. W. Cottrell, and R. K. Belew. Automatic combination of multiple ranked retrieval systems. In SIGIR '94, pages 173-181, 1994.
3. N. Belkin, C. Cool, W. Croft, and J. Callan. The effect of multiple query representations on information retrieval system performance. In SIGIR '93, pages 339-346, 1993.
4. D. Chickering, D. Heckerman, and C. Meek. A Bayesian approach to learning Bayesian networks with local structure. In UAI '97, pages 80-89, 1997.
5. M. Corporation. WinMine Toolkit v1.0. htt://research.microsoft.com/~dmax/WinMine/ContactInfo.html, 2001.
6. S. T. Dumais and H. Chen. Hierarchical classification of web content. In SIGIR '00, pages 256-263, 2000.
7. S. T. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In CIKM '98, pages 148-155, 1998.
8. D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering, and data visualization. Journal of Machine Learning Research, 1:49-75, 2000.
9. D. Hull, J. Pedersen, and H. Schuetze. Method combination for document filtering. In SIGIR '96, pages 279-287, 1996.
10. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In ECML '98, pages 137-142, 1998.
11. J. Katzer, M. McGill, J. Tessier, W. Frakes, and P. DasGupta. A study of the overlap among document representations. Information Technology: Research and Development, 1:261-274, 1982.
12. W. Lam and K.-Y. Lai. A meta-learning approach for text categorization. In SIGIR '01, pages 303-309, 2001.
13. L. S. Larkey and W. B. Croft. Combining classifiers in text categorization. In SIGIR '96, pages 289-297, 1996.
14. D. D. Lewis. A sequential algorithm for training text classifiers: Corrigendum and additional data. SIGIR Forum, 29(2):13-19, Fall 1995.
15. D. D. Lewis. Reuters-21578, distribution 1.0. http://www.daviddlewis.com/resources/testcollections/reuters21578, January 1997.
16. D. D. Lewis and W. A. Gale. A sequential algorithm for training text classifiers. In SIGIR '94, pages 3-12, 1994.
17. D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka. Training algorithms for linear text classifiers. In SIGIR '96, pages 298-306, 1996.
18. Y. Li and A. Jain. Classification of text documents. The Computer Journal, 41(8):537-546, 1998.
19. A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In Working Notes of AAAI 1998, Workshop on Learning for Text Categorization, pages 41-48, 1998.
20. J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers. MIT Press, 1999.
21. F. Provost and T. Fawcett. Robust classification for imprecise environments. Machine Learning, 42:203-231, 2001.
22. T. Rajashekar and W. Croft. Combining automatic and manual index representations in probabilistic retrieval. Journal of the American Society for Information Science, 6(4):272-283, 1995.
23. R. E. Schapire and Y. Singer. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39:135-168, 2000.
24. J. Shaw and E. Fox. Combination of multiple searches. In D. K. Harman, editor, TREC-3 Conference, number 500-225 in NIST Special Publication, pages 105-108, 1995.
25. K. Ting and I. Witten. Issues in stacked generalization. Journal of Artificial Intelligence Research, 10:271-289, 1999.
26. K. Toyama and E. Horvitz. Bayesian modality fusion: Probabilistic integration of multiple vision algorithms for head tracking. In ACCV 2000, Fourth Asian Conference on Computer Vision, 2000.
27. C. van Rijsbergen. Information Retrieval. Butterworths, London, 1979.
28. S. Weiss, C. Apte, F. Damerau, D. Johnson, F. Oles, T. Goets, and T. Hampp. Maximizing text-mining performance. IEEE Intelligent Systems, 14(4), 1999.
29. D. H. Wolpert. Stacked generalization. Neural Networks, 5:241-259, 1992.
30. Y. Yang, T. Ault, and T. Pierce. Combining multiple learning strategies for effective cross validation. In ICML '00, pages 1167-1182, 2000.
31. Y. Yang and X. Liu. A re-examination of text categorization methods. In SIGIR '99, pages 42-49, 1999.