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dc.contributor.author Daly, Ronan
dc.contributor.author Shen, Qiang
dc.date.accessioned 2009-07-02T08:33:14Z
dc.date.available 2009-07-02T08:33:14Z
dc.date.issued 2009-06
dc.identifier.citation Daly , R & Shen , Q 2009 , ' Learning Bayesian network equivalence classes with ant colony optimization ' Journal of Artificial Intelligence Research , vol 35 , pp. 391-447 . en
dc.identifier.issn 1943-5037
dc.identifier.other PURE: 107216
dc.identifier.other dspace: 2160/2536
dc.identifier.uri http://hdl.handle.net/2160/2536
dc.identifier.uri http://www.jair.org/media/2681/live-2681-4509-jair.pdf en
dc.description Daly, R., Shen, Q. (2009). Learning Bayesian network equivalence classes with ant colony optimization. Artificial Intelligence Research 35, 391-447. en
dc.description.abstract Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, 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. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes. en
dc.format.extent 57 en
dc.language.iso eng
dc.relation.ispartof Journal of Artificial Intelligence Research en
dc.title Learning Bayesian network equivalence classes with ant colony optimization en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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