Discretization of continuous features explained

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]

Software

This is a partial list of software that implement MDL algorithm.

See also

References

  1. Clarke . E. J. . Barton . B. A. . 10.1002/(SICI)1098-111X(200001)15:1<61::AID-INT4>3.0.CO;2-O . Entropy and MDL discretization of continuous variables for Bayesian belief networks . International Journal of Intelligent Systems . 15 . 61–92 . 2000 . 2008-07-10 .
  2. Fayyad, Usama M.; Irani, Keki B. (1993) Web site: 2014/35171 . Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. 29 July 2023 ., Proc. 13th Int. Joint Conf. on Artificial Intelligence (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. S. . Kotsiantis . D. Kanellopoulos . Discretization Techniques: A recent survey. GESTS International Transactions on Computer Science and Engineering . 32 . 1 . 2006 . 47–58. 10.1.1.109.3084.