A balanced ensemble approach to weighting classifiers for text classification

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 869-873

  Fung, Gabriel Pui Cheong ^a   Yu, Jeffrey Xu ^a   Wang, Haixun ^b   Cheung, David W ^c   Liu, Huan ^d  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c UNIVERSITY OF HONG KONG   (Hong Kong)

^d ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BALANCED COMBINATION FUNCTIONS; DYNAMIC CLASSIFIER WEIGHTING (DCW); TEXT CLASSIFICATION;

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; DECISION MAKING; PROBLEM SOLVING;

TEXT PROCESSING;

EID: 84878065641     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.2     Document Type: Conference Paper

References (20)

1. W. W. Cohen and Y. Singer. Context-sensitive learning methods for text categorization. ACM Transactions on Information Systems (TOIS), 17(2):141-173, 1999.
2. F. Debole and F. Sebastiani. An analysis of the relative hardness of Reuters-21578 subsets. Journal of the American Society for Information Science and Technology, 56(6):584-596, 2004.
3. G. Giacinto and F. Roli. Adaptive selection of Image classifiers. In Proceedings of the 9th International Conference on Image Analysis and Processing (ICIAP'97), pages 38-45, Florence, Italy, 1997.
4. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of 10th European Conference on Machine Learning (ECML'98), pages 137-142, Chemnitz, Germany, 1998.
5. K. B. Kevin Woods, W. Philip Kegelmeyer. Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 19(4):405-410, 1997.
6. W. Lam and K.-Y. Lai. A meta-learning approach for text categorization. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'01), pages 303-309, New Orleans, Louisiana, USA, 2001.
7. L. S. Larkey and W B. Croft. Combining classifiers in text categorization. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'96), pages 289-297, Zurich, Switzerland, 1996.
8. Y. H. Li and A. K. Jain. Classification of text documents. The Computer Journal, 41(8):537-546, 1998.
9. R. Liere and P. Tadepalli. Active learning with committees for text categorization. In Proceedings of 14th National Conference on Artificial Intelligence (AAAI'97), pages 591-596, Providence, Rhode Island, 1997.
10. A. McCallum and K. Nigam. A Comparison of Event Models for Naive Bayes Text Classification. In The 15th National Conference on Artificial Intelligence (AAAI'98) Workshop on Learning for Text Categorization, 1998.
11. A. Moschitti. A study on optimal parameter tuning for rocchio text classifier. In Proceedings of the 25th European Conference on Information Retrieval Research (ECIR'03), pages 420-435, Pisa, Italy, 2003.
12. H. T. Ng, W B. Goh, and K. L. Low. Feature selection, perception learning, and a usability case study for text categorization. In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'97), pages 67-73, Philadelphia, PA, USA, 1997.
13. E. Rasmussen. Clustering algorithm. In W. B. Freakes and R. Baeza-Yates, editors, Information Retrieval Data Structures & Algorithms, pages 419-442. Prentice Hall PTR, 1992.
14. O. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management (IPM), 24(5):513-523, 1988.
15. R. E. Schapire and Y. Singer. BoosTexter: a boosting-based system for text categorization. Machine Learning, 39(2-3):135-168, 2000.
16. R. E. Schapire, Y. Singer, and A. Singhal. Boosting and Rocchio applied to text filtering. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98), pages 215-223, Melbourne, Australia, 1998.
17. F. Seabastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1-47, 2002.
18. S. M. Weiss, C. Apte, F. J. Damerau, D. E. Johnson, F. J. Oles, T. Goetz, and T. Hampp. Maximizing text-mining performance. IEEE Intelligent Systems, 14(4):63-69, 1999.
19. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, second edition, 2005.
20. Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '99), pages 42-49, Berkeley, California, USA, 1999.