End-to-End Bayesian Networks Exact Learning in Shared Memory

Bayesian networks are important Machine Learning models with many practical applications in, e.g., biomedicine and bioinformatics. The problem of Bayesian networks learning is <inline-formula><tex-math notation="LaTeX">\mathcal {NP}</tex-math> <mml:math><mml:mi m...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 35; H. 4; S. 634 - 645
Hauptverfasser: Karan, Subhadeep, Sayed, Zainul Abideen, Zola, Jaroslaw
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bayes methods
Bayesian analysis
Bayesian networks
Bioinformatics
Directed acyclic graph
Dynamic programming
exact learning
Lattices
Machine learning
Memory tasks
Networks
Optimization
Search problems
Task analysis
task parallelism
ISSN:1045-9219, 1558-2183
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Zusammenfassung:Bayesian networks are important Machine Learning models with many practical applications in, e.g., biomedicine and bioinformatics. The problem of Bayesian networks learning is <inline-formula><tex-math notation="LaTeX">\mathcal {NP}</tex-math> <mml:math><mml:mi mathvariant="script">NP</mml:mi></mml:math><inline-graphic xlink:href="zola-ieq1-3366471.gif"/> </inline-formula>-hard and computationally challenging. In this article, we propose practical parallel exact algorithms to learn Bayesian networks from data. Our approach uses shared-memory task parallelism to realize exploration of dynamic programming lattices emerging in Bayesian networks structure learning, and introduces several optimization techniques to constraint and partition the underlying search space. Through extensive experimental testing we show that the resulting method is highly scalable, and it can be used to efficiently learn large globally optimal networks.
Bibliographie:ObjectType-Article-1
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2024.3366471