A greedy algorithm for supervised discretization

Journal of Biomedical Informatics

Volumn 37, Issue 4, 2004, Pages 285-292

  Butterworth, Richard ^a   Simovici, Dan A ^a   Santos, Gustavo S ^b   Ohno Machado, Lucila ^b  

^a UNIVERSITY OF MASSACHUSETTS BOSTON   (United States)

^b HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

Boundary points; Generalized entropy; Metric; Na ve Bayes classifiers

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; DATA BASE; ENTROPY; MATHEMATICAL COMPUTING; PRIORITY JOURNAL;

EID: 4744362954     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2004.07.006     Document Type: Article

References (19)

1. Dougherty J, Kohavi R, Sahami M. Supervised and unsupervised discretization of continuous features. In: Proc. of the 12th International Conference on Machine Learning; 1995. p. 194-202
2. I. Kononenko Naive Bayes classifier and continuous attributes Informatica 16 1992 1 8
3. I. Kononenko Inductive and Bayesian learning in medical diagnosis Appl. Artif. Intell. 7 1993 317 337
4. Fayyad UM. On the Induction of Decision Trees for Multiple Concept Learning. Ph.D. thesis, University of Michigan; 1991
5. Fayyad UM, Irani K. Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc. of the 12th International Joint Conference on Artificial Intelligence; 1993. p. 1022-27
6. Yang Y, Webb GI. Proportional k-interval discretization for naïve-Bayes classifiers. In: Proc. of the 12th European Conference on Machine Learning; 2001. p. 564-75
7. Yang Y, Webb GI. Weighted proportional k-interval discretization for naïve-Bayes classifiers. In: Proc. of the PAKDD; 2003
8. Robnik M, Kononenko I. Discretization of continuous attributes using relief. In: Proc. of ERK-95; 1995. p. 149-152
9. I.H. Witten, and E. Frank Data Mining-Practical Machine Learning Tools and Techniques with Java Implementations 2000 Morgan Kaufmann San Francisco
10. R.L. de Màntaras A distance-based attribute selection measure for decision tree induction Mach. Learn. 6 1991 81 92
11. Devijer PA. Entropie quadratique et reconnaisance des formes. In: Computer Oriented Learning Processes, Proceedings of the NATO Advanced Study Institute, Château de Bonas, France; 1974. p. 257-78
12. Z. Daróczy Generalized information functions Inf. Control 16 1970 36 51
13. J.H. Havrda, and F. Charvat Quantification methods of classification processes: concepts of structural α-entropy Kybernetica 3 1967 30 35
14. D.A. Simovici, and S. Jaroszewicz An axiomatization of partition entropy IEEE Trans. Inf. Theory 48 2002 2138 2142
15. Simovici D, Jaroszewicz S. Generalized conditional entropy and decision trees. In: Extraction et Gestion des connaissances - EGC 2003, Lavoisier, Paris; 2003. p. 363-80
16. Cerquides J, de Màntaras RL. Proposal and empirical comparison of a parallelizable distance-based discretization method. In: Proc. of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD'97). Newport Beach, CA; 1997. p. 139-42
17. Blake CL, Merz CJ. UCI Repository of machine learning databases, University of California, Irvine, Dept. of Information and Computer Sciences; 1998. Available from: 〈http://www.ics.uci.edu/~mlearn/MLRepository. html〉
18. J. Khan, J.S. Wei, and M. Ringner Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks Nat. Med. 7 2001 673 679
19. L. Ohno-Machado, S.A. Vinterbo, and G. Webber Classification of gene expression data using fuzzy logic J. Intell. Fuzzy Syst. 12 2002 19 24