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Line 16: </ref> Mechanisms for discretizing continuous data include [[Usama Fayyad\|Fayyad]] & Irani's MDL method,<ref>Fayyad, Usama M.; Irani, Keki B. (1993) {{cite web\|hdl=2014/35171 \| url = https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf \| title = Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning\| date = 29 July 2023 }}, ''Proc. 13th Int. Joint Conf. on Artificial Intelligence'' (Q334 .I571 1993), pp. 1022-1027 </ref> which uses [[mutual information]] 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\| 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\|citeseerx = 10.1.1.109.3084}}</ref>

Discretization of continuous features: Difference between revisions