Discretization of continuous features
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).
Many machine learning algorithms are known to produce better models by discretizing continuous attributes.
- Clarke, E. J.; Barton, B. A. (2000). "Entropy and MDL discretization of continuous variables for Bayesian belief networks" (PDF). 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.
- 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
- 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
- Kotsiantis, S.; Kanellopoulos, D (2006). "Discretization Techniques: A recent survey" (PDF). GESTS International Transactions on Computer Science and Engineering 32 (1): 47–58.
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