An efficient global discretization method

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

Volumn 1394, Issue , 1998, Pages 383-384

  Ho, K M ^a   Scott, P D ^a  

^a UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84897030453     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-64383-4_35     Document Type: Conference Paper

References (5)

1. Fayyad, U.M., & Irani, K.B. (1993). Multi-interval discretization of continuous-value attributes for classification learning. In Proc. 13th International Joint Conference on Artificial Intelligence, pp. 1022-1027. San Francisco: Morgan Kaufmann.
2. Ho, K.M., & Scott, P.D. (1997a). Zeta: A global method for discretization of continuous variables. In KDD97: 3rd International Conference of Knowledge Discovery and Data Mining, pp. 191-194, Newport Beach, CA.
3. Ho, K.M., & Scott, P.D. (1997b). An Efficient Global Discretization Method. Technical Report CSM-296, Department of Computer Science, University of Essex, England.
4. Kohavi, R., John, G., Long, R., Manley, D. & Pfleger, K. (1994), MLC++: A machine learning library in C++. In Tools with Artificial Intelligence, IEEE Computer Society Press, pp. 740-743.
5. Quinlan, J.R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4 pp. 77-90.