Discretization of continuous features

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In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density estimation. It is a form of discretization in general and also of binning, as in making a histogram. Whenever continuous data is discretized, there is always some amount of discretization error. The goal is to reduce the amount to a level considered negligible for the modeling purposes at hand.

Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies).[1]

Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method,[2] which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others[3]

Many machine learning algorithms are known to produce better models by discretizing continuous attributes.[4]

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References[edit]

  1. ^ Clarke, E. J.; Barton, B. A. (2000). "Entropy and MDL discretization of continuous variables for Bayesian belief networks". International Journal of Intelligent Systems 15: 61. doi:10.1002/(SICI)1098-111X(200001)15:1<61::AID-INT4>3.0.CO;2-O. Retrieved 2008-07-10. 
  2. ^ Fayyad, Usama M.; Irani, Keki B. (1993) "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning". hdl:2014/35171.  , Proceedings of the International Joint Conference on Uncertainty in AI (Q334 .I571 1993), pp. 1022-1027
  3. ^ Dougherty, J.; Kohavi, R. ; Sahami, M. (1995). "Supervised and Unsupervised Discretization of Continuous Features". In A. Prieditis & S. J. Russell, eds. Work. Morgan Kaufmann, pp. 194-202
  4. ^ Kotsiantis, S.; Kanellopoulos, D (2006). "Discretization Techniques: A recent survey". GESTS International Transactions on Computer Science and Engineering 32 (1): 47–58.