On changing continuous attributes into ordered discrete attributes

  • J. Catlett
Part 3: Numeric And Statistical Approaches
Part of the Lecture Notes in Computer Science book series (LNCS, volume 482)


The large real-world datasets now commonly tackled by machine learning algorithms are often described in terms of attributes whose values are real numbers on some continuous interval, rather than being taken from a small number of discrete values. Many algorithms are able to handle continuous attributes, but learning requires far more CPU time than for a corresponding task with discrete attributes. This paper describes how continuous attributes can be converted economically into ordered discrete attributes before being given to the learning system. Experimental results from a wide variety of domains suggest this change of representation does not often result in a significant loss of accuracy (in fact it sometimes significantly improves accuracy), but offers large reductions in learning time, typically more than a factor of 10 in domains with a large number of continuous attributes.


Discretisation empirical concept learning induction of decision trees 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • J. Catlett
    • 1
  1. 1.Basser Department of Computer ScienceUniversity of SydneyAustralia

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