Particle swarm optimisation for learning Bayesian networks

J. Cowie; L. Oteniya; R. Coles

Particle swarm optimisation for learning Bayesian networks

J. Cowie, L. Oteniya, R. Coles

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the World Congress on Engineering, WCE 2007, London, UK, 2-4 July, 2007
Subtitle of host publication	Lecture notes in engineering and computer science, Volume 1
Editors	S. I. Ao, L. Gelman
Place of Publication	Hong Kong
Publisher	International Association of Engineers
Pages	71-76
Number of pages	6
ISBN (Print)	9789889867157
Publication status	Published - 2007
Event	World Congress on Engineering 2007 - South Kensington Campus, Imperial College London, London, United Kingdom Duration: 2 Jul 2007 → 4 Jul 2007 http://www.iaeng.org/WCE2007/

Conference

Conference	World Congress on Engineering 2007
Abbreviated title	WCE 2007
Country/Territory	United Kingdom
City	London
Period	2/07/07 → 4/07/07
Internet address	http://www.iaeng.org/WCE2007/

Cite this

@inproceedings{7c6d91e42ec348dda70d633b3d78a218,

title = "Particle swarm optimisation for learning Bayesian networks",

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.",

author = "J. Cowie and L. Oteniya and R. Coles",

year = "2007",

language = "English",

isbn = "9789889867157",

pages = "71--76",

editor = "Ao, {S. I.} and L. Gelman",

booktitle = "Proceedings of the World Congress on Engineering, WCE 2007, London, UK, 2-4 July, 2007",

publisher = "International Association of Engineers",

address = "China",

note = "World Congress on Engineering 2007, WCE 2007 ; Conference date: 02-07-2007 Through 04-07-2007",

url = "http://www.iaeng.org/WCE2007/",

}

Cowie, J, Oteniya, L & Coles, R 2007, Particle swarm optimisation for learning Bayesian networks. in SI Ao & L Gelman (eds), Proceedings of the World Congress on Engineering, WCE 2007, London, UK, 2-4 July, 2007: Lecture notes in engineering and computer science, Volume 1. International Association of Engineers, Hong Kong, pp. 71-76, World Congress on Engineering 2007, London, United Kingdom, 2/07/07.

Particle swarm optimisation for learning Bayesian networks. / Cowie, J.; Oteniya, L.; Coles, R.
Proceedings of the World Congress on Engineering, WCE 2007, London, UK, 2-4 July, 2007: Lecture notes in engineering and computer science, Volume 1. ed. / S. I. Ao; L. Gelman. Hong Kong: International Association of Engineers, 2007. p. 71-76.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Particle swarm optimisation for learning Bayesian networks

AU - Cowie, J.

AU - Oteniya, L.

AU - Coles, R.

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

M3 - Conference contribution

SN - 9789889867157

SP - 71

EP - 76

BT - Proceedings of the World Congress on Engineering, WCE 2007, London, UK, 2-4 July, 2007

A2 - Ao, S. I.

A2 - Gelman, L.

PB - International Association of Engineers

CY - Hong Kong

T2 - World Congress on Engineering 2007

Y2 - 2 July 2007 through 4 July 2007

ER -

Particle swarm optimisation for learning Bayesian networks

Abstract

Conference

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