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dc.contributor.authorMayo, Michael
dc.contributor.authorSun, Quan
dc.coverage.spatialBeijing, China
dc.date.accessioned2014-10-21T20:51:20Z
dc.date.available2014
dc.date.available2014-10-21T20:51:20Z
dc.date.issued2014
dc.identifier.citationMayo, M., & Sun, Q. (2014). Evolving artificial datasets to improve interpretable classifiers. In 2014 IEEE Congress on Evolutionary Computation (CEC), 6-11 July 2014, Beijing, China (pp. 2367–2374). Washington, DC, USA: IEEE. http://doi.org/10.1109/CEC.2014.6900238en
dc.identifier.urihttps://hdl.handle.net/10289/8844
dc.description.abstractDifferential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple interpretable supervised classifiers (specifically, Naive Bayes and linear Support Vector Machines) are trained (i) directly on “real” data, as would be the normal case, and (ii) indirectly, using special artificial datasets derived from real data via evolutionary optimization. The results across several challenging test problems show that supervised classifiers trained indirectly using our novel evolution-based approach produce models with superior predictive classification performance. Besides presenting the accuracy of the learned models, we also analyze the sensitivity of our artificial data optimization process to Differential Evolution's parameters, and then we examine the statistical characteristics of the artificial data that is evolved.
dc.format.extent2367 - 2374
dc.format.mimetypeapplication/pdf
dc.publisherIEEE
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceEvolutionary Computation (CEC), 2014 IEEE Congress on
dc.subjectMachine learning
dc.titleEvolving artificial datasets to improve interpretable classifiers
dc.typeConference Contribution
dc.identifier.doi10.1109/CEC.2014.6900238
dc.relation.isPartOf2014 IEEE Congress on Evolutionary Computation (CEC)
pubs.begin-page2367
pubs.elements-id115684
pubs.elements-id115684
pubs.end-page2374
pubs.finish-date2014-07-11en_NZ
pubs.place-of-publicationWashington, DC, USA
pubs.publication-statusPublished
pubs.start-date2014-07-06en_NZ


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