Parallel structural learning of Bayesian networks: Iterative divide and conquer algorithm based on structural fusion

Learning Bayesian Networks (BNs) from high-dimensional data is a complex and time-consuming task. Although the literature includes approaches based on horizontal (instances) or vertical (variables) partitioning, none can guarantee the same theoretical properties as the Greedy Equivalence Search (GES...

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Vydáno v:	Knowledge-based systems Ročník 296; s. 111840
Hlavní autoři:	Laborda, Jorge D., Torrijos, Pablo, Puerta, José M., Gámez, José A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 19.07.2024
Témata:	Bayesian network fusion/aggregation Bayesian network learning Distributed machine learning High-dimensional problems Bayesian network fusion/aggregation Distributed machine learning Bayesian network learning High-dimensional problems
ISSN:	0950-7051, 1872-7409
On-line přístup:	Získat plný text
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