Synonyms
Definition
Discretization is a process that transforms a numeric attribute into a categorical attribute. Under discretization, a new categorical attribute X′ is formed from and replaces an existing numeric attribute X. Each value x′ of X′ corresponds to an interval (a,b] of X. Any original numeric value x of X that belongs to (a,b] is replaced by x′. The boundary values of formed intervals are often called “cut points.”
Motivation and Background
Many learning systems require categorical data, while many data are numeric. Discretization allows numeric data to be transformed into categorical form suited to processing by such systems. Further, in some cases effective discretization can improve either computational or prediction performance relative to learning from the original numeric data.
Taxonomy
The following taxonomy identifies many key dimensions along which alternative discretization techniques can be distinguished.
Supervised vs. Unsupervised (Dougherty et al. 1995...
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Recommended Reading
Bay SD (2000) Multivariate discretization of continuous variables for set mining. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 315–319
Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Proceedings of the twelfth international conference on machine learning, pp 194–202
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Hsu CN, Huang HJ, Wong TT (2000) Why discretization works for naïve Bayesian classifiers. In: Proceedings of the seventeenth international conference on machine learning, pp 309–406
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Yang Y, Webb G (2001) Proportional k-interval discretization for naive-Bayes classifiers. In: Proceedings of the twelfth European conference on machine learning, pp 564–575
Yang Y, Webb G (2002) Non-disjoint discretization for naive-Bayes classifiers. In: Proceedings of the nineteenth international conference on machine learning, pp 666–673
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Yang, Y. (2017). Discretization. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_221
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_221
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