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dc.contributor.authorMayfield, Helen J
dc.contributor.authorBertone, Edoardo
dc.contributor.authorSmith, Carl
dc.contributor.authorSahin, Oz
dc.date.accessioned2019-08-26T05:34:43Z
dc.date.available2019-08-26T05:34:43Z
dc.date.issued2020
dc.identifier.issn0378-4754
dc.identifier.doi10.1016/j.matcom.2019.07.005
dc.identifier.urihttp://hdl.handle.net/10072/386685
dc.description.abstractBayesian networks have become a popular modelling technique in many fields, however there are several design decisions that, if poorly made, can result in models with insufficient evidence to make good predictions. One such decision is how to discretise the continuous nodes. The lack of a commonly accepted algorithm for achieving this makes it a difficult task for novice data modellers. We present a structure aware discretisation algorithm that minimises the number of missing values in the conditional probability tables by taking into account the network structure. It also prevents users from having to specify the exact number of bins. Results from two water quality case studies in south-east Queensland showed that the algorithm has potential to improve the discretisation process over equal case discretisation and demonstrates the suitability of Bayesian networks for this field.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofjournalMathematics and Computers in Simulation
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchPhysical Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode02
dc.subject.fieldofresearchcode08
dc.titleUse of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationMayfield, HJ; Bertone, E; Smith, C; Sahin, O, Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions, Mathematics and Computers in Simulation, 2019
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2019-08-26T05:24:25Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2019 IMACS/Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorSahin, Oz
gro.griffith.authorBertone, Edoardo
gro.griffith.authorMayfield, Helen


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