Khiops: A Statistical Discretization Method of Continuous Attributes

Authors:
Marc Boulle
Affiliations:
France Telecom R&D, 2, Avenue Pierre Marzin, 22300 Lannion, France. marc.boulle@francetelecom.com
Venue:
Machine Learning
Year:
2004

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Abstract

In supervised machine learning, some algorithms are restricted to discrete data and have to discretize continuous attributes. Many discretization methods, based on statistical criteria, information content, or other specialized criteria, have been studied in the past. In this paper, we propose the discretization method Khiops,1 based on the chi-square statistic. In contrast with related methods ChiMerge and ChiSplit, this method optimizes the chi-square criterion in a global manner on the whole discretization domain and does not require any stopping criterion. A theoretical study followed by experiments demonstrates the robustness and the good predictive performance of the method.