Weighted average pointwise mutual information for feature selection in text categorization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3721 LNAI, Issue , 2005, Pages 252-263

  Schneider, Karl Michael ^a  

^a UNIVERSITY OF PASSAU   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; ESTIMATION; FEATURE EXTRACTION; HEURISTIC METHODS; PROBLEM SOLVING;

FEATURE SELECTION; MODEL SELECTION; MUTUAL INFORMATION; TEXT CATEGORIZATION; WEIGHTED AVERAGE POINTWISE MUTUAL INFORMATION;

INFORMATION RETRIEVAL;

EID: 33646417914     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564126_27     Document Type: Conference Paper

References (14)

1. John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In Cohen, W.W., Hirsh, H., eds.: Machine Learning: Proceedings of the Eleventh International Conference, San Francisco, CA, Morgan Kaufmann Publishers (1994) 121- 129
2. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proc. 14th International Conference on Machine Learning (ICML-97), (1997) 412-420
3. McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: Learning for Text Categorization: Papers from the AAAI Workshop, AAAI Press (1998) 41-48 Technical Report WS-98-05.
4. Eyheramendy, S., Lewis, D.D., Madigan, D.: On the Naive Bayes model for text categorization. In Bishop, C.M., Frey, B.J., eds.: AI & Statistics 2003: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics. (2003) 332- 339
5. Friedman, J.H: On bias, variance, 0/1-loss, and the curse-of- dimensionality. Data Mining and Knowledge Discovery 1 (1997) 55-77
6. Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley, New York (1991)
7. Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Computational Linguistics 16 (1990) 22-29
8. Rennie, J.D.M.: Improving multi-class text classification with Naive Bayes. Master's thesis, Massachusetts Institute of Technology (2001)
9. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18 (1975) 613-620
10. Dhillon, I.S., Mallela, S., Kumar, R.: A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research 3 (2003) 1265-1287
11. Lang, K.: NewsWeeder: Learning to filter netnews. In: Proc. 12th International Conference on Machine Learning (ICML-95), Morgan Kaufmann (1995) 331-339
12. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., Slattery, S.: Learning to construct knowledge bases from the World Wide Web. Artificial Intelligence 118 (2000) 69-113
13. Apte, C., Damerau, F., Weiss, S.M.: Towards language independent automated learning of text categorization models. In: Proc. 17th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '94). (1994) 23-30
14. Forman, G.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3 (2003) 1289-1305