Boosting to correct inductive bias in text classification

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2002, Pages 348-355

  Liu, Yan ^a   Yang, Yiming ^a   Carbonell, Jaime ^a  

^a Carnegie Mellon University   (United States)

Author keywords

Boosting; Inductive bias; Machine learning; Text classification

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA STRUCTURES; DECISION THEORY; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION; PROBABILITY;

DECISION TREES; ROCCHIO CLASSIFIER; SUPPORT VECTOR MACHINES; TEXT CLASSIFICATION;

TEXT PROCESSING;

EID: 0037480963     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/584792.584850     Document Type: Conference Paper

References (28)

1. C. Apte, F. Damerau, and S. Weiss. Towards language independent automated learning of text categorization models. In Proceedings of the 17th Annual ACM/SIGIR conference, 1994.
2. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2):105-139, 1999.
3. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
4. C. Buckley, G. Salton, and J. Allan, The effect of adding relevance information in a relevance feedback environment. pages 292-300, London, 1994.
5. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, pages 23-37, 1995.
6. Y. Freund and R.E. Schapire. Experiments with a new boosting algorithm. In International Conference on Machine Learning, pages 148-156, 1996.
7. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting, 1998.
8. T. Joachims. Making large-scale svm learning practical. In Advances in Kernel Methods - Support Vector Learning, Cambridge, USA, 1998. M.I.T. Press.
9. T. Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In European Conference on Machine Learning (ECML), pages 137-142, Berlin, 1998. Springer.
10. T. Joachims. Transductive inference for text classification using support vector machines. In International Conference on Machine Learning (ICML'99), San Francisco, CA, 1999. Morgan Kaufmann.
11. G. Lebanon and J. Lafferty. Boosting and maximum likelihood for exponential models. In Neural Information Processing Systems (NIPS), 2001.
12. D. Lewis, F. Li, T. Rose, and Y. Yang. The reuters corpus volume i as a text categorization test collection. In SIGIR 2002 (submitted).
13. D.D. Lewis. Evaluating and optimizing autonomous text classification systems. In E. A. Fox, P. Ingwersen, and R. Fidel, editors, SIGIR '95: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 246-254, New York, 1995. Association for Computing Machinery.
14. A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization, 1998.
15. T. Mitchell. Machine Learning. McGraw Hill, 1996.
16. J.R. Quinlan. Bagging, boosting, and c4.5. Proceedings of the 13th National Conference on Artificial Intelligence on Machine Learning, pages 322-330.
17. J.R. Quinlan. C4.5: Programs for machine learning. Morgan Kaufmann.
18. M. Rogati and Y. Yang. High performing and scalable feature selection for text categorization. In AAAI-02 (submitted), 2002.
19. G. Salton, editor. The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1971.
20. R. Schapire and Y. Singer. Boosttexter: Aboosting-based system for text categorization. In Machine Learning, volume 39(1/3), pages 135-168, 2000.
21. R.E. Schapire. Theoretical views of boosting and applications. In Proc. 10th International Conference on Algorithmic Learning Theory - ALT '99, volume 1720, pages 13-25. Springer-Verlag, 1999.
22. R.E. Schapire, Y. Singer, and A. Singhal. Boosting and rocchio applied to text filtering. In Proceedings of the Twenty-first Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 215-223, New York, 1998. The Association for Computing Machinery.
23. L.G. Valiant. A theory of the learnable. In ACM Symposium on Theory of Computing, pages 436-445, 1984.
24. C. van Rijsbergen. Information Retrieval. Butterworths, London 1979.
25. S. Weiss, C. Apte, F. Damerau, D. Johnson, F. Oles, T. Goets, and T. Hampp. Maximizing text-mining performance. IEEE Intelligent Systems, Special Issue on Applications of Intelligent Information Retrieval, 14(4):63-69, 1999.
26. Y. Yang. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval, 1(1/2):67-88, 1999.
27. Y. Yang and X. Liu. A re-examination of text categorization methods. In The 22th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99), pages 42-49, 1999.
28. Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In J. D. H. Fisher, editor, The Fourteenth International Conference on Machine Learning (ICML'97), pages 412-420. Morgan Kaufmann, 1997.