Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier

DOI10.1007/s10994-020-05869-5zbMath1496.68274OpenAlexW3007586552MaRDI QIDQ782457

Dan Halbersberg, Maydan Wienreb, Boaz Lerner

Publication date: 27 July 2020

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

Full work available at URL: https://doi.org/10.1007/s10994-020-05869-5

zbMATH Keywords

information measuresstructure learningBayesian network classifiersordinal classificationclass imbalance0/1 loss function

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items (3)

The role of mutual information in variational classifiers ⋮ extended_rmcv.m ⋮ Ordinal regression with explainable distance metric learning based on ordered sequences

Uses Software

• Matlab
• UCI-ml
• LIBSVM
• BNT
• PRTools
• HandTill2001
• SMOTE

Cites Work

• Learning Bayesian network classifiers by risk minimization
• Classification trees for ordinal variables
• A Bayesian method for the induction of probabilistic networks from data
• A characterization of the Dirichlet distribution through global and local parameter independence
• Bayesian network classifiers
• Learning Bayesian networks: The combination of knowledge and statistical data
• Comparing two \(K\)-category assignments by a \(K\)-category correlation coefficient
• Multi-Classifier Systems: Review and a roadmap for developers
• An Information Measure for Classification
• A simple generalisation of the area under the ROC curve for multiple class classification problems
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