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Soft Computing
Erschienen in: Soft Computing 6/2013

01.06.2013 | Methodologies and Application

An artificial bee colony algorithm for learning Bayesian networks

verfasst von: Junzhong Ji, Hongkai Wei, Chunnian Liu

Erschienen in: Soft Computing | Ausgabe 6/2013

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Abstract

One basic approach to learn Bayesian networks (BNs) from data is to apply a search procedure to explore the set of candidate networks for the database in light of a scoring metric, where the most popular stochastic methods are based on some meta-heuristic mechanisms, such as Genetic Algorithm, Evolutionary Programming and Ant Colony Optimization. In this paper, we have developed a new algorithm for learning BNs which employs a recently introduced meta-heuristic: artificial bee colony (ABC). All the phases necessary to tackle our learning problem using this meta-heuristic are described, and some experimental results to compare the performance of our ABC-based algorithm with other algorithms are given in the paper.
Vorheriger Artikel GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning
Nächster Artikel An efficient algorithm for high-dimensional function optimization

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Metadaten
Titel
An artificial bee colony algorithm for learning Bayesian networks
verfasst von
Junzhong Ji
Hongkai Wei
Chunnian Liu
Publikationsdatum
01.06.2013
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 6/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0966-6

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