A targeted Bayesian network learning for classification

A. Gruber, I. Ben-Gal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


A targeted Bayesian network learning (TBNL) method is proposed to account for a classification objective during the learning stage of the network model. The TBNL approximates the expected conditional probability distribution of the class variable. It effectively manages the trade-off between the classification accuracy and the model complexity by using a discriminative approach, constrained by information theory measurements. The proposed approach also provides a mechanism for maximizing the accuracy via a Pareto frontier over a complexity–accuracy plane, in cases of missing data in the data-sets. A comparative study over a set of classification problems shows the competitiveness of the TBNL mainly with respect to other graphical classifiers.

Original languageEnglish
Pages (from-to)243-261
Number of pages19
JournalQuality Technology and Quantitative Management
Issue number3
StatePublished - 4 May 2019


FundersFunder number
Israel Science Foundation1362/10


    • AI
    • Bayesian classifiers
    • complexity–accuracy trade-off
    • information theory
    • machine learning
    • target-oriented learning


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