Particle Swarm Optimisation for learning Bayesian Networks

Citation

Cowie J, Oteniya L & Coles R (2007) Particle Swarm Optimisation for learning Bayesian Networks. In: Ao S, Gelman L, Hukins DWL, Hunter A & Korsunsky AM (eds.) Proceedings of the World Congress on Engineering 2007, Vol I, WCE 2007, July 2 - 4, 2007, London, U.K.. Lecture Notes in Engineering and Computer Science. ICCIIS 2007, World Congress on Engineering, WCE 2007, London, 02.07.2007-04.07.2007. Newswood Limited / International Association of Engineers (IAENG), pp. 71-76. http://www.iaeng.org/publication/WCE2007/

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