Using ant colony optimisation in learning Bayesian network equivalence classes

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dc.contributor.author Shen, Qiang
dc.contributor.author Daly, Ronan
dc.contributor.author Aitken, Stuart
dc.date.accessioned 2008-01-15T21:16:41Z
dc.date.available 2008-01-15T21:16:41Z
dc.date.issued 2006
dc.identifier.citation Shen , Q , Daly , R & Aitken , S 2006 , ' Using ant colony optimisation in learning Bayesian network equivalence classes ' pp. 111-118 . en
dc.identifier.other PURE: 74429
dc.identifier.other dspace: 2160/437
dc.identifier.uri http://hdl.handle.net/2160/437
dc.description R. Daly, Q. Shen and S. Aitken. Using ant colony optimisation in learning Bayesian network equivalence classes. Proceedings of the 2006 UK Workshop on Computational Intelligence, pages 111-118. en
dc.description.abstract Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed on the ACO construction graph. Secondly, moves can be given in terms of arbitrary identifiers. The algorithm is implemented and tested. The results show that ACO performs better than a greedy search whilst searching in the space of equivalence classes. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Using ant colony optimisation in learning Bayesian network equivalence classes en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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