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The Computer Journal 1996 39(8):688-691; doi:10.1093/comjnl/39.8.688
© 1996 by British Computer Society
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A Bayesian Discretizer for Real-Valued Attributes

X. Wu *

Department of Software Development, Monash University, 900 Dandenong Road, Melbourne 3145, Australia

Discretization of real-valued attributes into nominal intervals has been an important area for symbolic induction systems because many real world classification tasks involve both symbolic and numerical attributes. Among various supervised and unsupervised discretization methods, the information gain-based methods have been widely used and cited. This paper designs a new discretization method, called the Bayesian discretizer, and compares its performance with the information gain methods implemented in C4.5 and HCV (Version 2.0). Over the seven datasets tested, the Bayesian discretizer has the best results of four of them in terms of predictive accuracy.

Received March 29, 1996. revised October 25, 1996.

* Department of Software Development, Monash University, 900 Dandenong Road, Melbourne 3145, Australia Email: xindonginsect.sd.monash.edu.au


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