Abstract
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.
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© 1991 Springer-Verlag Berlin Heidelberg
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Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017012
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DOI: https://doi.org/10.1007/BFb0017012
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