Implications of the Dirichlet assumption for discretization of continuous variables in naive Bayesian classifiers

DOI10.1023/A:1026367023636zbMath1089.68096OpenAlexW1742152372MaRDI QIDQ703061

Chun-Nan Hsu, Hung-Ju Huang, Tzu-Tsung Wong

Publication date: 19 January 2005

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

Full work available at URL: https://doi.org/10.1023/a:1026367023636

zbMATH Keywords

discretizationcontinuous variablesinterval dataDirichlet distributionslazy discretizationNaive Bayesian classifiersperfect aggregation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

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Individual attribute prior setting methods for naïve Bayesian classifiers ⋮ Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation ⋮ Multiple Bayesian discriminant functions for high-dimensional massive data classification ⋮ Discretization for Naive-Bayes learning: managing discretization bias and variance ⋮ Optimal bandwidth selection for re-substitution entropy estimation

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