A discretization algorithm based on Class-Attribute Contingency Coefficient

Information Sciences

Volumn 178, Issue 3, 2008, Pages 714-731

  Tsai, Cheng Jung ^a   Lee, Chien I ^b   Yang, Wei Pang ^c  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b NATIONAL UNIVERSITY OF TAINAN   (Taiwan)

^c NATIONAL DONG HWA UNIVERSITY   (Taiwan)

Author keywords

Classification; Contingency coefficient; Data mining; Decision tree; Discretization

Indexed keywords

DATA MINING; DATA STRUCTURES; DECISION TREES; LEARNING SYSTEMS;

CONTINGENCY COEFFICIENTS; DISCRETIZATION ALGORITHMS;

LEARNING ALGORITHMS;

EID: 35748943218     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2007.09.004     Document Type: Article

References (38)

1. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, University of California, Department of Information and Computer Science, Irvine, CA, 1998. Available from: .
2. Boulle M. Khiops: a statistical discretization method of continuous attributes. Machine Learning 55 1 (2004) 53-69
3. Cardoso G., and Gomide F. Newspaper demand prediction and replacement model based on fuzzy clustering and rules. Information Sciences 177 21 (2007) 4799-4809
4. Ching J.Y., Wong A.K.C., and Chan K.C.C. Class-dependent discretization for inductive learning from continuous and mixed mode data. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 7 (1995) 641-651
5. Chiu D., Wong A., and Cheung B. Information discovery through hierarchical maximum entropy discretization and synthesis. In: Piatetsky-Shapiro G., and Frawley W.J. (Eds). Knowledge Discovery in Databases (1991), AAAI Press 125-140
6. Cios K.J., and Kurgan L.A. Hybrid inductive machine learning: an overview of clip algorithms. In: Jain L.C., and Kacprzyk J. (Eds). New Learning Paradigms in Soft Computing (2001), Physica-Verlag (Springer) 276-322
7. Cios K.J., and Kurgan L.A. CLIP4: hybrid inductive machine learning algorithm that generates inequality rules. Information Science 163 1-3 (2004) 37-83
8. Clark P., and Niblett T. The CN2 algorithm. Machine Learning 3 4 (1989) 261-283
9. Cohen S., Rokach L., and Maimon O. Decision-tree instance-space decomposition with grouped gain-ratio. Information Sciences 177 17 (2007) 3592-3612
10. Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
11. J. Dougherty, R. Kohavi, M. Sahami, Supervised and unsupervised discretization of continuous features, in: Proceeding of Twelfth International Conference on Machine Learning, 1995, pp. 194-202.
12. Fayyad U.M., and Irani K.B. On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8 (1992) 87-102
13. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceeding of Thirteenth International Conference on Artificial Intelligence, 1993, pp. 1022-1027.
14. Berzal F., Cubero J.C., Mari{dotless}́n N., and Sánchez D. Building multi-way decision trees with numerical attributes. Information Sciences 165 1-2 (2004) 73-90
15. Gómez D., Montero J., and Yáñez J. A coloring fuzzy graph approach for image classification. Information Sciences 176 24 (2006) 3645-3657
16. Han J., and Kamber M. Data Mining: Concepts and Techniques (2001), Morgan Kaufman
17. Hidekazu T., Sayed A.E., Masao F., Kazuhiro M., Kazuhiko T., and Aoe J.I. Estimating sentence types in computer related new product bulletins using a decision tree. Information Sciences 168 1-4 (2004) 185-200
18. K.A. Kaufman, R.S. Michalski, Learning from inconsistent and noisy data: the AQ18 approach, in: Proceeding of Eleventh International Symposium on Methodologies for Intelligent Systems, 1999.
19. R. Kerber, ChiMerge: discretization of numeric attributes, in: Proceeding of Ninth International Conference on Artificial Intelligence, 1992, pp. 123-128.
20. L. Kurgan, K.J. Cios, Fast Class-Attribute Interdependence Maximization (CAIM) Discretization Algorithm, in: Proceeding of International Conference on Machine Learning and Applications, 2003, pp. 30-36.
21. Kurgan L., and Cios K.J. CAIM discretization algorithm. IEEE Transactions on Knowledge and Data Engineering 16 2 (2004) 145-153
22. C.I. Lee, C.J. Tsai, Y.R. Yang, W.P. Yang, A top-down and greedy method for discretization of continuous attributes, in: Proceedings of Fourth International Conference on Fuzzy Systems and Knowledge Discovery, Haikou, China, 2007.
23. Liu H., and Setiono R. Feature selection via discretization. IEEE Transactions on Knowledge and Data Engineering 9 4 (1997) 642-645
24. Liu H., Hussain F., Tan C.L., and Dash M. Discretization: an enabling technique. Journal of Data Mining and Knowledge Discovery 6 4 (2002) 393-423
25. S. Mehta; S. Parthasarathy, H. Yang, Correlation preserving discretization data mining, in: Proceeding of Fourth IEEE International Conference on Data Mining, 2004, pp. 479-482.
26. R.S. Michalski, I. Mozetic, J. Hong, N. Lavrac, The multipurpose incremental learning system AQ15 and its testing application to three medical domains, in: Proceeding of Fifth National Conference on Artificial Intelligence, 1986, pp. 1041-1045.
27. A. Paterson, T.B. Niblett, ACLS manual, Edinburgh: Intelligent Terminals, Ltd., 1987.
28. B. Pfahringer, Compression-based discretization of continuous attributes, in: Proceeding of Twelfth International Conference on Machine Learning, 1995, pp. 456-463.
29. R. Quinlan, Data Mining Tools, 2005. .
30. R. Rastogi, K. Shim, PUBLIC: a decision tree classifier that integrates building and pruning, in: Proceedings of the 24th International Conference on Very Large Databases, 1998, pp. 404-415.
31. Shih D.H., Chiang H.S., and Yen C.D. Classification methods in the detection of new malicious emails. Information Sciences 172 1-2 (2005) 241-261
32. Srinivasa K.G., Venugopal K.R., and Patnaik L.M. A self-adaptive migration model genetic algorithm for data mining applications. Information Sciences 177 20 (2007) 4295-4313
33. Su C.T., and Hsu J.H. An extended chi2 algorithm for discretization of real value attributes. IEEE Transactions on Knowledge and Data Engineering 17 3 (2005) 437-441
34. Tay F., and Shen L. A modified chi2 algorithm for discretization. IEEE Transactions on Knowledge and Data Engineering 14 3 (2002) 666-670
35. Wong A.K.C., and Chiu D.K.Y. Synthesizing statistical knowledge from incomplete mixed-mode data. IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (1987) 796-805
36. Q. Wu, D.A. Bell, T.M. McGinnity, G. Prasad, G. Qi, X. Huang, Improvement of decision accuracy using discretization of continuous attributes, in: Proceedings of the Third International Conference on Fuzzy Systems and Knowledge Discovery, Lecture Notes in Computer Science 4223, 2006, pp. 674-683.
37. Yager R.R. An extension of the naive Bayesian classifier. Information Sciences 176 5 (2006) 577-588
38. Zadrożny S., and Kacprzyk J. Computing with words for text processing: an approach to the text categorization. Information Sciences 176 4 (2006) 415-437