Discretization of continuous features: Difference between revisions

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Revision as of 20:26, 12 June 2012

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Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method<ref>Fayyad, Usama M.; Irani, Keki B. (1993) {{cite web|id={{hdl|2014/35171}} |title=Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning}}, ''Proceedings of the International Joint Conference on Uncertainty in AI'' (Q334 .I571 1993), pp. 1022-1027 </ref>, which uses [[information gain]] to recursively define the best bins, CAIM, CACC, Ameva, and many others<ref>Dougherty, J.; Kohavi, R. ; Sahami, M. (1995). "[http://robotics.stanford.edu/users/sahami/papers-dir/disc.pdf Supervised and Unsupervised Discretization of Continuous Features]". In A. Prieditis & S. J. Russell, eds. ''Work''. Morgan Kaufmann, pp. 194-202}}</ref>

Many machine learning algorithms are known to produce better models by discretizing continuous attributes.<ref>{{cite journal|url=http://www.math.upatras.gr/~esdlab/en/members/kotsiantis/discretization%20survey%20kotsiantis.pdf | first1=S. |last1=Kotsiantis |first2= D| last2= Kanellopoulos |title=Discretization Techniques: A recent survey|journal= GESTS International Transactions on Computer Science and Engineering |volume=32 |issue=1 |year=2006 |pages= 47–58}}</ref>