Research output

Conference Paper (in Formal Publication) ()

Particle Swarm Optimisation for learning Bayesian Networks

Citation
Cowie J, Oteniya L & Coles R (2007) Particle Swarm Optimisation for learning Bayesian Networks In: Ao Sio-Iong, Gelman Len, Hukins David W L, Hunter Andrew, Korsunsky Alexander M (ed.) Proceedings of the World Congress on Engineering 2007, Vol I, WCE 2007, July 2 - 4, 2007, London, U.K., Newswood Limited / International Association of Engineers (IAENG). ICCIIS 2007, World Congress on Engineering, WCE 2007, 2.7.2007 - 4.7.2007, London, pp. 71-76.

Abstract
This paper discusses the potential of Particle Swarm Optimisation (PSO) for inducing Bayesian Networks (BNs). Specifically, we detail two methods which adopt the search and score approach to BN learning. The two algorithms are similar in that they both use PSO as the search algorithm, and the K2 metric to score the resulting network. The difference lies in the way networks are constructed. The CONstruct And Repair (CONAR) algorithm generates structures, validates, and repairs if required, and the REstricted STructure (REST) algorithm, only permits valid structures to be developed. Initial experiments indicate that these approaches produce promising results when compared to other BN learning strategies.

Keywords
Particle Swarm Optimisation; Bayesian Network Construction

StatusPublished
EditorAo Sio-Iong, Gelman Len, Hukins David W L, Hunter Andrew, Korsunsky Alexander M
AuthorsCowie Julie, Oteniya Lloyd, Coles Richard
Title of seriesLecture Notes in Engineering and Computer Science
Publication date07/2007
URLhttp://www.iaeng.org/publication/WCE2007/
PublisherNewswood Limited / International Association of Engineers (IAENG)
ISBN 978-988-98671-5-7
LanguageEnglish
© University of Stirling FK9 4LA Scotland UK • Telephone +44 1786 473171 • Scottish Charity No SC011159
My Portal