Text classification without labeled negative documents

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2005, Pages 594-605

  Fung, Gabriel Pui Cheong ^a   Yu, Jeffrey Xu ^a   Lu, Hongjun ^b   Yu, Philip S ^c  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

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

Author keywords

[No Author keywords available]

Indexed keywords

PARTITION BASED HEURISTICS; TEXT CLASSIFICATION;

HEURISTIC METHODS; PROBLEM SOLVING; PROGRAM DOCUMENTATION;

CLASSIFICATION (OF INFORMATION);

EID: 28444431513     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2005.139     Document Type: Conference Paper

References (24)

1. D. Bennett and Demiritz. A semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11, 1998.
2. J. Bockhorst and M. Craven. Exploiting relations among concepts to acquire weakly labeled training data. In Proceedings of the 19th International Conference on Machine Learning, 2002.
3. P. Bradley and U. Fayyad. Refining initial points for k-means clustering. In Proceedings of the 15th International Conference on Machine Learning, 1998.
4. D. R. Cutting, D. R. Karger, J. O. Pederson, and J. W. Tukey. Scatter/gather a cluster-based approach to browsing large document collections. In Proceedings of the 15th International Conference on Research and Development in Information Retrieval, 1992.
5. R. Ghani. Combining labeled and unlabeled data for multiclass text categorization. In Proceedings of the 19th International Conference on Machine Learning, 2002.
6. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of 10th European Conference on Machine Learning, 1998.
7. B. Larsen and C. Aone. Fast and effective text mining using linear-time document clustering. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, 1999.
8. W. S. Lee and B. Liu. Learning with positive and unlabeled example using weighted logistics regression. In Proceedings of 20th International Conference on Machine Learning, 2003.
9. X. Li and B. Liu. Learning to classify texts using positive and unlabeled data. In Proceedings of 2003 International Joint Conference on Artificial Intelligence, 2003.
10. B. Liu, Y. Dai, X. Li, W. S. Lee, and P. S. Yu. Building text classifiers using positive and unlabeled examples. In Proceedings of 3rd International Conference on Data Mining, 2003.
11. B. Liu, P. S. Yu, and X. Li. Partially supervised classification of text documents. In Proceedings of 19th International Conference on Machine Learning, 2002.
12. A. McCallum and K. Nigam. A comparison of event models for naive bayes text classification. In The 15th National Conference on Artificial Intelligence Workshop on Learning for Text Categorization, 1998.
13. D. C. Montgomery. Introduction to Statistical Quality Control. Wiley Text Books, forth edition, 2000.
14. D. C. Montogomery and G. C. Runger. Applied Statistics and Probability for Engineers. John Wiley & Sons, Inc., second edition, 1999.
15. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using em. Machine Learning, 39, 2000.
16. J. Rocchio. Relevance feedback information retrieval. In G. Salton, editor, The Smart retrieval System - experiments in automatic document processing, pages 313-323. Prentice Hall, 1971.
17. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513-523, 1988.
18. H. Schutze, D. A. Hull, and J. O. Pedersen. A comparison of classifiers and document representations for the routing problem. In Proceedings of the 18th International Conference on Research and Development in Information Retrieval, 1995.
19. F. Seabastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1): 1-47, 2002.
20. Y. Yang. A study on thresholding strategies for text categorization. In Proceedings of the 24nd International Conference on Research and Development in Information Retrieval, 2001.
21. Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of the 22nd International Conference on Research and Development in Information Retrieval, 1999.
22. H. Yu, K. C.-C. Chang, and J. Han. Heterogeneous learner for web page classification. In Proceedings of 2nd International Conference on Data Mining, 2003.
23. H. Yu, J. Han, and K. C.-C. Chang. Pebl: Positive example based learning for web page classification using svm. In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining, 2003.
24. T. Zhang. The value of unlabeled data for classification problems. In Proceedings of the 17th International Conference on Machine Learning, 2000.