On changing continuous attributes into ordered discrete attributes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1991

AUTHORS

J. Catlett

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. More... »

PAGES

164-178

References to SciGraph publications

Book

TITLE

Machine Learning — EWSL-91

ISBN

3-540-53816-X

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0017012

DOI

http://dx.doi.org/10.1007/bfb0017012

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020632806


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