MODL: a Bayes optimal discretization method for continuous attributes

DOI10.1007/s10994-006-8364-xzbMath1470.68086OpenAlexW2001592424MaRDI QIDQ851870

Marc Boullé

Publication date: 22 November 2006

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-006-8364-x

zbMATH Keywords

discretizationdata analysisdata miningmachine learningBayesianism

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Statistical aspects of big data and data science (62R07) Computational aspects of data analysis and big data (68T09)

Related Items

Mint: MDL-based approach for mining interesting numerical pattern sets ⋮ Variable selection for Naïve Bayes classification ⋮ ABBA: adaptive Brownian bridge-based symbolic aggregation of time series ⋮ Unsupervised discretization by two-dimensional MDL-based histogram ⋮ Fast and fully-automated histograms for large-scale data sets ⋮ Learning data discretization via convex optimization ⋮ Fuzzy partitioning of continuous attributes through discretization methods to construct fuzzy decision tree classifiers ⋮ An effective discretization method for disposing high-dimensional data ⋮ Optimum simultaneous discretization with data grid models in supervised classification: a Bayesian model selection approach

Uses Software

• UCI-ml
• C4.5
• MODL

Cites Work

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• On Grouping for Maximum Homogeneity
• Minimum description length induction, Bayesianism, and Kolmogorov complexity
• FUSINTER: A Method for Discretization of Continuous Attributes
• Multivariate discretization for set mining