Using hypothesis margin to boost centroid text classifier

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2007, Pages 398-403

  Tan, Songbo ^a   Cheng, Xueqi ^a  

^a ICT   (China)

Author keywords

Data mining; Information retrieval; Machine learning; Text classification

Indexed keywords

DATA MINING; ERROR ANALYSIS; INFORMATION RETRIEVAL SYSTEMS; MATHEMATICAL MODELS; SUPPORT VECTOR MACHINES;

BENCHMARK COLLECTIONS; CENTROID TEXT CLASSIFIER; GLOBAL OBJECTIVE FUNCTION; SVM CLASSIFIER;

TEXT PROCESSING;

EID: 35248816378     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1244002.1244096     Document Type: Conference Paper

References (28)

1. E. Han and G. Karypis. Centroid-Based Document Classification Analysis & Experimental Result. PKDD 2000.
2. Shrikanth Shankar and George Karypis. Weight adjustment schemes for a centroid-based classifier. TextMining Workshop, KDD, 2000.
3. Y. Yang, X. Lin, A re-examination of text categorization methods. SIGIR. 1999, 42-49.
4. Y. Yang. An evaluation of statistical approaches to text categorization. Information Retrieval, 1999, 1(1): 76-88.
5. Masand, B., Linoff, G. and Waltz., D. Classifying news stories using memory based reasoning. SIGIR. 1992, 59-64.
6. Thorsten Joachims. A Probabilistic Analysis of the Rocchio Algorithm, with TFIDF for Text Categorization. ICML. 1997, 143-151.
7. Robert Schapire, Yoram Singer and Amit Singhal. Boosting and Rocchio Applied to Text Filtering (1998). SIGIR. 1998, 215-223.
8. D. D. Lewis. Naive (Bayes) at forty: the independence assumption in. information retrieval. ECML. 1998, 4-15.
9. T. Kalt and W. B. Croft. A new probabilistic model of text classification and retrieval. Tech. Rep. TR98-18, University of Massachusetts Center for Intelligent Information Retrieval, 1996.
10. Andrew McCallum, Kamal Nigam. A Comparison of Event Models for Naive Bayes Text Classification. AAAI/ICML-98 Workshop on Learning for Text Categorization[C]. Menlo Park, CA: AAAI Press. 1998, 41-48.
11. P.P.T.M. van Mun. Text Classification in Information Retrieval using Winnow.
12. T., Zhang. Regularized Winnow Methods. Advances in Neural Information Processing Systems. 2001, 703-709.
13. Schapire, R. E. and Singer, Y. Boostexter: A boosting-based system for text categorization. Machine Learning, 2000,39, 135-168.
14. R.E. Schapire, Y. Freund, P. Bartlett and W.S. Lee. Boosting the Margin: A New Explanation for the Effectiveness of Voting Method. The Annals of Statistics. 1998, 26(5): 1651-1686.
15. Kjersti Aas, Line Eikvil. Text Categorisation: A Survey. Raport NR 941, Norwegian Computing Center, 1999, 15.
16. T. Joachims. Text categorization with support vector machines: learning with many relevant features. ECML. 1998, 137-142.
17. Godbole, S., Sarawagi, S. and Chakrabarti, S. Scaling multi-class support vector machine using inter-class confusion. SIGKDD. 2002, 513-518.
18. Haoran Wu, Tong Heng Phang, Bing Liu and Xiaoli Li. A Refinement Approach to Handling Model Misfit in Text Categorization. SIGKDD. 2002, 207-216.
19. Yan Liu, Yiming Yang and Jaime Carbonell. Boosting to Correct Inductive Bias in Text Classification. CIKM. 2002,348-355.
20. Y. Yang. A study on thresholding strategies for text categorization. SIGIR 2001, 137-145.
21. Crammer, K., Gilad-Bachrach, R., Navot, A., & Tishby, N. (2002). Margin analysis of the lvq algorithm. Proc. 17'th Conference on Neural Information Processing Systems.
22. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
23. Ran Gilad-Bachrach, Amir Navot and Naftali Tishby. Margin Based Feature Selection - Theory and Algorithms. ICML, 2004.
24. W. Hersh, C. Buckley, T. Leone, and D. Hickam. OHSUMED: An interactive retrieval evaluation and new large test collection for research. SIGIR. 1994, 192-201.
25. Y. Yang and Jan O. Pedersen. A Comparative Study on Feature Selection in Text Categorization. ICML. 1997, 412-420.
26. D. D. Lewis, R. E. Schapire, J. P. Callan, and R. Papka. Training algorithms for linear text classifiers. SIGIR. 1996, 298-306.
27. SALTON, G. AND BUCKLEY, C. 1988. Term-weighting approaches in automatic text retrieval. Inform. Process. Man. 24, 5, 513-523.
28. F. Sebastiani. Machine learning in automated text categorization, ACM Computing Surveys, 34(1), 1-47, March 2002.