Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning

DOI10.1007/s10994-011-5275-2zbMath1238.68138OpenAlexW2156621559WikidataQ62833579 ScholiaQ62833579MaRDI QIDQ420929

Liguang Zhu, Fei Zheng, Pramuditha Suraweera, Geoffrey I. Webb

Publication date: 23 May 2012

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

Full work available at URL: https://doi.org/10.1007/s10994-011-5275-2

zbMATH Keywords

classificationnaive Bayesfeature selectionAODEsemi-naive Bayes

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis ⋮ Extracting credible dependencies for averaged one-dependence estimator analysis ⋮ A bias-variance based heuristic for constructing a hybrid logistic regression-naïve Bayes model for classification

Uses Software

• UCI-ml
• WEKA
• ElemStatLearn

Cites Work

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• Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification
• Bayesian network classifiers
• Not so naive Bayes: Aggregating one-dependence estimators
• Iterative Bayes
• Classification using hierarchical Naïve Bayes models
• Approximations of the critical region of the fbietkan statistic
• A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
• The elements of statistical learning. Data mining, inference, and prediction

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