Automatically computed document dependent weighting factor facility for Naïve Bayes classification

Expert Systems with Applications

Volumn 37, Issue 12, 2010, Pages 8471-8478

  Lee, Lam Hong ^a   Isa, Dino ^b  

^a Jalan Universiti   (Malaysia)

^b UNIVERSITY OF NOTTINGHAM MALAYSIA CAMPUS   (Malaysia)

Author keywords

ACDD weighting factor facility; Na ve bayes; Text document classification

Indexed keywords

CLASSIFICATION (OF INFORMATION); ECONOMIC AND SOCIAL EFFECTS; TEXT PROCESSING;

BAYES CLASSIFICATION; CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; CLASSIFICATION PERFORMANCE; CLASSIFICATION RATES; CLASSIFIED DOCUMENTS; TEXT DOCUMENT CLASSIFICATIONS; WEIGHTING FACTORS;

INFORMATION RETRIEVAL SYSTEMS;

EID: 77957854582     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.05.030     Document Type: Article

References (27)

1. Adami, G., Avesani, P., & Sona, D. (2005). Clustering documents into web directory for bootstrapping a supervised classification. Data and Knowledge Engineering, 54(3), 301-325.
2. Al-Mubaid, H., & Umair, S. A. (2006). A new text categorization technique using distributional clustering and learning logic. IEEE Transactions on Knowledge and Data Engineering, 18(9), 1156-1165.
3. Apte, C., Damerau, F., & Weiss, S. (1994a). Towards language independent automated learning of text categorization models. In: Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval, pp. 23-30.
4. Apte, C., Damerau, F., & Weiss, S. (1994b). Automated learning of decision rules for text categorization. ACM Transactions on Information Systems (TOIS), 12(3), 233-251.
5. Brücher, H., Knolmayer, G., & Mittermayer, M.A. (2002). Document classification methods for organizing explicit knowledge. Research Group Information Engineering, Institute of Information Systems, University of Bern, Engehaldenstrasse 8, CH - 3012 Bern, Switzerland. (University Technical Report).
6. Dhillon, I., Mallela, S., & Kumar, R. (2002). Enhanced word clustering for hierarchical text classification. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002, pp. 191-200.
7. Diligenti, M., Maggini, M., & Rigutini, L. (2003). Learning similarities for text documents using neural networks. In: Proceedings of the IAPR - TC3 International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Italy.
8. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2-3), 103-130.
9. Eyheramendy, S., Genkin, A., Ju, W.H., Lewis, D., & Madigan, D. (2003). Sparse Bayesian classifiers for text categorization. Department of Statistics, Rutgers University, 2003. (University Technical Report). URL: http://www.stat.rutgers.edu/madigan/PAPERS/jicrd-v13.pdf [2006-05-15].
10. Greiner, R., & Schaffer, J. (2001). AIxploratorium - decision trees. Department of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, Canada, 2001. URL: http://www.cs.ualberta.ca/aixplore/learning/DecisionTrees [2007- 4-14].
11. Han, E.H., Karypis, G., & Kumar, V. (1999). Text categorization using weighted ddjusted k-nearest neighbor classification. Department of Computer Science and Engineering. Army HPC Research Centre, University of Minnesota, Minneapolis, USA. (University Technical Report).
12. Isa, D., Lee, L. H., Kallimani, V. P., & Rajkumar, R. (2008a). Polychotomiser for casebased reasoning beyond the traditional bayesian classification approach. Computer and Information Science, Canadian Center of Science and Education (CCSE), 1(1), 57-68.
13. Isa, D., Lee, L. H., Kallimani, V. P., & Rajkumar, R. (2008b). Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Transactions on Knowledge and Data Engineering (TKDE), 20(9), 1264-1272.
14. Isa, D., Lee, L. H., Kallimani, V. P., & Rajkumar, R. (2008c). Text document preprocessing with the Bayes formula for classification based on the vector space model. Computer and Information Science, Canadian Center of Science and Education (CCSE), 1(4), 79-90.
15. Isa, D., Kallimani, V. P., & Lee, L. H. (2009). Using the self-organizing map for clustering of text documents. Expert Systems With Applications (ESWA), 36(5), 9584-9591.
16. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning (ECML-98), pp. 137-142.
17. Joachims, T. (1999). Making large-scale SVM learning practical. Advances in Kernel Methods - Support Vector Learning, 169-184.
18. Kim, S.B., Rim, H.C., Yook, D.S., & Lim, H.S. (2002). Effective methods for improving naÏve bayes text classification. Lecture notes in Artificial Intelligence, Vol.2417; In: Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence, Heidelberg: Springer, pp. 414-423.
19. Lin, Y. (1999). Support vector machines and the Bayes rule in classification. Technical Report No. 1014, Department of Statistics, University of Wiscousin, Madison. (University Technical Report).
20. McCallum, A., & Nigam, K. (2003). A comparison of event models for naÏve Bayes text classification. Journal of Machine Learning Research(3), 1265-1287.
21. Nigam, K., Lafferty, J., & McCallum, A. (1999). Using maximum entropy for text classification. In: Proceedings of the IJCAI-99 Workshop on Machine Learning for Information Filtering, pp. 61-67.
22. Ozgur, A., Ozgur, L., & Gungor, T. (2005). Text categorization with class-based and corpus-based keyword selection. Lecture notes in Computer Science, Vol.3733; In: Proceedings of the 20th International Symposium on Computer and Information Sciences, Springer-Verlag, pp. 607-616.
23. Rish, I. (2001). An empirical study of the naÏve Bayes classifier. In: Proceedings of the IJCAI-01 Workshop on Empirical Methods in Artificial Intelligence, pp. 41-46.
24. Rish, I., Hellerstein, J., & Thathachar, J. (2001). An analysia of data characteristics that affect naÏve Bayes performance. IBM T.J. Watson Research Center 30 Saw Mill River Road, Hawthorne, NY 10532, USA. (IBM Technical Report).
25. Ruiz, M., & Srinivasan, P. (1998). Automatic text categorization using neural network. In: Proceedings of the 8th ASIS SIG/CR Workshop on Classification Research, pp. 59-72.
26. Torkkola, K. (2001). Linear discriminant analysis in document classification. In: Proceedings of the IEEE ICDM-2001 Workshop on Text Mining (TextDM'2001), San Jose, CA.
27. Yang, Y.M., & Liu, X. (1999). 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), pp. 42-49.