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dc.contributor.authorMayo, Michaelen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.date.accessioned2019-05-07T22:58:37Z
dc.date.available2018en_NZ
dc.date.available2019-05-07T22:58:37Z
dc.date.issued2020en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12514
dc.description.abstractCan we evolve better training data for machine learning algorithms? To investigate this question we use population-based optimisation algorithms to generate artificial surrogate training data for naive Bayes for regression. We demonstrate that the generalisation performance of naive Bayes for regression models is enhanced by training them on the artificial data as opposed to the real data. These results are important for two reasons. Firstly, naive Bayes models are simple and interpretable but frequently underperform compared to more complex "black box" models, and therefore new methods of enhancing accuracy are called for. Secondly, the idea of using the real training data indirectly in the construction of the artificial training data, as opposed to directly for model training, is a novel twist on the usual machine learning paradigm.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectcs.AIen_NZ
dc.subjectMachine learning
dc.titleImproving Naive Bayes for regression with optimised artificial surrogate dataen_NZ
dc.typeJournal Article
dc.identifier.doi10.1080/08839514.2020.1726615
dc.relation.isPartOfApplied Artificial Intelligenceen_NZ
pubs.begin-page484
pubs.begin-page484
pubs.elements-id203836
pubs.end-page514


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