Learning Bayesian networks in the space of structures by estimation of distribution algorithms

DOI10.1002/int.10084zbMath1028.68162OpenAlexW2066796977WikidataQ62637456 ScholiaQ62637456MaRDI QIDQ4416287

Iñaki Inza, Pedro Larrañaga, Rosa Blanco

Publication date: 31 July 2003

Published in: International Journal of Intelligent Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/int.10084

zbMATH Keywords

genetic algorithmunivariate marginal distribution algorithmpopulation-based incremental learning

Mathematics Subject Classification ID

Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (12)

Experimental comparisons with respect to the usage of the promising relations in EDA-based causal discovery ⋮ Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood ⋮ Ant colony optimization for learning Bayesian networks. ⋮ Structural learning of Bayesian networks using local algorithms based on the space of orderings ⋮ Learning Structure Illuminates Black Boxes – An Introduction to Estimation of Distribution Algorithms ⋮ Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes ⋮ A review on evolutionary algorithms in Bayesian network learning and inference tasks ⋮ Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms ⋮ Bayesian network learning algorithms using structural restrictions ⋮ Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms ⋮ Comments on: ``Hybrid semiparametric Bayesian networks ⋮ Quantum approximate optimization algorithm for Bayesian network structure learning

Uses Software

• TETRAD

Cites Work

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• A Mathematical Theory of Communication
• A Bayesian method for the induction of probabilistic networks from data
• Estimating the dimension of a model
• The size distribution for Markov equivalence classes of acyclic digraph models.
• Learning Bayesian networks: The combination of knowledge and statistical data
• Probabilistic Networks and Expert Systems
• Feature subset selection by Bayesian networks: A comparison with genetic and sequential algorithms

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