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).[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.
- discretize4crf tool designed to work with popular CRF implementations (C++)
- mdlp in the R package discretization
- Discretize in the R package RWeka
See also
References
- ↑ Clarke, E. J.; Barton, B. A. (2000). "Entropy and MDL discretization of continuous variables for Bayesian belief networks". International Journal of Intelligent Systems 15: 61–92. doi:10.1002/(SICI)1098-111X(200001)15:1<61::AID-INT4>3.0.CO;2-O. http://sci2s.ugr.es/keel/pdf/specific/articulo/IJIS00.pdf. Retrieved 2008-07-10.
- ↑ Fayyad, Usama M.; Irani, Keki B. (1993) "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning". 29 July 2023. https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf., Proc. 13th Int. Joint Conf. on Artificial Intelligence (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". GESTS International Transactions on Computer Science and Engineering 32 (1): 47–58.
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