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 2007
Editors	S.I. Ao, Len Gelman, David WL Hukins, Andrew Hunter, A. M. Korsunsky
Publisher	Newswood
Pages	71-76
Number of pages	6
Volume	1
ISBN (Electronic)	9789889867157, 9789889867126
Publication status	Published - 30 Jun 2007
Event	World Congress on Engineering - London, United Kingdom Duration: 2 Jul 2007 → 4 Jul 2007 http://www.iaeng.org/WCE2007/

Conference

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

Keywords

Particle Swarm Optimisation
Bayesian Network Construction

ASJC Scopus subject areas

General Engineering

Documents and Links

Cite this

@inproceedings{0beb5050306040cf9e3fb78a7235e68f,

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.",

keywords = "Particle Swarm Optimisation, Bayesian Network Construction",

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

year = "2007",

month = jun,

day = "30",

language = "English",

volume = "1",

pages = "71--76",

editor = "S.I. Ao and Len Gelman and Hukins, {David WL} and Andrew Hunter and Korsunsky, {A. M.}",

booktitle = "Proceedings of the World Congress on Engineering 2007",

publisher = "Newswood ",

note = "World Congress on Engineering, 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, DWL Hukins, A Hunter & AM Korsunsky (eds), Proceedings of the World Congress on Engineering 2007. vol. 1, Newswood , pp. 71-76, World Congress on Engineering, London, United Kingdom, 2/07/07. <http://www.iaeng.org/publication/WCE2007/WCE2007_pp71-76.pdf>

TY - GEN

T1 - Particle swarm optimisation for learning Bayesian networks

AU - Cowie, J.

AU - Oteniya, L.

AU - Coles, R.

PY - 2007/6/30

Y1 - 2007/6/30

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.

KW - Particle Swarm Optimisation

KW - Bayesian Network Construction

UR - http://www.iaeng.org/publication/WCE2007/

M3 - Conference contribution

VL - 1

SP - 71

EP - 76

BT - Proceedings of the World Congress on Engineering 2007

A2 - Ao, S.I.

A2 - Gelman, Len

A2 - Hukins, David WL

A2 - Hunter, Andrew

A2 - Korsunsky, A. M.

PB - Newswood

T2 - World Congress on Engineering

Y2 - 2 July 2007 through 4 July 2007

ER -

Particle swarm optimisation for learning Bayesian networks

Abstract

Conference

Keywords

ASJC Scopus subject areas

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