A global optimal algorithm for class-dependent discretization of continuous data

Intelligent Data Analysis

Volumn 8, Issue 2, 2004, Pages 151-170

  Liu, Lili ^a   Wong, Andrew K C ^a   Wang, Yang ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b Pattern Discovery Software Systems Ltd   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFIERS; DATA MINING; DECISION TREES; DISCRETE EVENT SIMULATION; ITERATIVE METHODS; MACHINE LEARNING; TREES (MATHEMATICS);

DISCRETIZATION PROCESS; FRACTIONAL PROGRAMMING; INDUCTIVE MACHINE LEARNING; ITERATIVE DYNAMIC PROGRAMMING; NAIVE BAYES CLASSIFIERS; NORMALIZED MUTUAL INFORMATION; OPTIMIZATION PROBLEMS; PATTERN CLASSIFICATION PROBLEMS;

DYNAMIC PROGRAMMING;

EID: 22944432786     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2004-8204     Document Type: Article

References (31)

1. S.D. Bay, Multivariate Discretization for Set Mining, Knowledge and Information Systems 3(4) (2001), 491-512.
2. J. Catlett, On Changing Continuous Attributes into Ordered Discrete Attributes, Proceedings 5th European Working Session on Learning, Y. Kodratooe, Porto, Portugal, March 1991, pp. 164-178.
3. J.Y. Ching, A.K.C. Wong and K.C.C. Chan, Class-Dependent Discretization for Inductive Learning From Continuous Data, IEEE Transactions on Pattern Analysis and Machine Intelligence 17(7) (1995), 641-651.
4. D. Chiu, A.K.C. Wong and B. Cheung, Information Discovery Through Hierarchical Maximum Entropy, Journal of Experimental and Theoretical Artificial Intelligence 2 (1990), 117-129.
5. P. Clark and T. Biblett, the cn2 induction algorithm, Machine Learing Journal 3(4) (1989), 261-283.
6. P. Clark and R. Boswell, Rule induction with CN2: Some recent improvements, Proceedings of the Fifth European Working Session on Learning, Berlin, Germany, 1991, pp. 151-163.
7. J. Dougherty, R. Kohavi and M. Sahami, Supervised, Unsupervised Discretization of Continuous Features, International Conference on Machine Learning, 1995, 194-202.
8. U.M. Fayyad and K.B. Irani, On the Handling of Continuous-Valued Attributes in Decision Tree Generation, Machine Learning 8(1) (1992).
9. K.M. Ho and P.D. Scott, Zeta: A Global Method for Discretization of Continuous Variables, Knowledge Discovery and Data Mining, D. Heckerman, H. Mannila, D. Pregibon and R. Uthurusamy, AAAI Press, 1997, 191-194.
10. R.C. Holte, Very Simple Classification Rules PerformWell on Most Commonly Used Datasets, Machine Learning 11(1) (1993).
11. R. Kerber, Chi Merge: Discretization of Numeric Attributes, In Proceedings of the 9th International Conference on Artificial Intelligence, 1992, 123-128.
12. R. Kohavi, G. John, R. Long, D. Manley and K. Pfleger, MLC++: A Machine Learning Library in C, 1994
13. T. Kohonen, self-Organization and Associative Memory, Springer-Verlag, Berlin, Germany, 1989.
14. L. Kurgan and K.J. Cios, Discretization Algorithm that Uses Class-Attribute Interdependence Maximization, In Proceedings of the 2001 International Conference on Artificial Intelligence (IC-AI 2001), 980-987, Las Vegas, Nevada, June 2001.
15. P. Langley, Induction of Recursive Bayesian Classifiers, ECML93, P. Brazdil, Berlin, Germany, 1993, 153-164.
16. P. Langley,W. Iba and K. Thompson, An Analysis of Bayesian Classifiers, National Conference on Artificial Intelligence, San Jose, California, 1992, pp. 223-228.
17. H. Liu and R. Setiono, Feature Selection via Discretization, IEEE Transactions on Knowledge and Data Engineering 9(4) (1997), 642-645.
18. P.M. Murphy and D.W. Aha, UCI repository of machine learning databases, 1994.
19. A. Paterson and T.B. Niblett, ACLS Manual, Technical report, Intelligent Terminals Ltd., Edindurg 1987.
20. B. Pfahringer, Compression-Based Discretization of Continuous Attributes, In International Conference on Machine Learning, San Francisco, CA, 1995, pp. 456-463.
21. J.R. Quinlan, Induction of decision trees, Machine Learing 1(1) (1986), 81-106.
22. J.R. Quinlan, Simplifying decision trees, International journal of Man-Machine Studies 27(3) (1987), 221-234.
23. J.R. Quinlan, C4.5: Programs for Machine Learning, San, Mateo CA, Morgan Kauffman, 1993.
24. M. Richeldi and M. Rossotto, Class-driven statistical discretization of continuous attributes(extended abstract), Springer-Verlag, Berlin, Heidelberg, 1995, pp. 335-342.
25. M. Sniedovich, Dynamic Programming, Chapter appendix, Marcel Dekker, 1992, pp. 348-350.
26. J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley, 1974.
27. C.C. Wang and A.K.C. Wong, Classification of Discrete-Valued Data with Feature Space Transformation, IEEE Transactions on Automatic Control 24(3) (1979), 434-437.
28. A.K.C. Wong, Information Pattern Analysis, Synthesis and Discovery, Course Notes, University of Waterloo, Waterloo, Ontario, Canada, 1998.
29. A.K.C. Wong, Wrong and D.K.Y. Chiu, Synthesizing statistical knowledge from incomplete mixedmode data, IEEE Transactions on Pattern Analysis and Machine Learing 9(6) (November 1987), 796-805.
30. A.K.C.Wong and D.K.Y. Chiu, Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data, IEEE Transactions on Pattern Analysis and Machine Intelligence 9(6) (November 1987), 796-805.
31. A.K.C. Wong and C.C. Wang, DECA-A Discrete-Valued Data Clustering Algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence 1(4) (1979), 342-349.