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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorNevill-Manning, Craig G.
dc.date.accessioned2008-10-20T22:50:15Z
dc.date.available2008-10-20T22:50:15Z
dc.date.issued1995-04
dc.identifier.citationHolmes, G. & Nevill-Manning, C. G. (1995). Feature selection via the discovery of simple classification rules. (Working paper 95/10). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1088
dc.description.abstractIt has been our experience that in order to obtain useful results using supervised learning of real-world datasets it is necessary to perform feature subset selection and to perform many experiments using computed aggregates from the most relevant features. It is, therefore, important to look for selection algorithms that work quickly and accurately so that these experiments can be performed in a reasonable length of time, preferably interactively. This paper suggests a method to achieve this using a very simple algorithm that gives good performance across different supervised learning schemes and when compared to one of the most common methods for feature subset selection.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectfeature subset selectionen_US
dc.subjectsupervised learningen_US
dc.subject1Ren_US
dc.subjectfilter modelen_US
dc.subjectwrapper modelen_US
dc.titleFeature selection via the discovery of simple classification rulesen_US
dc.typeWorking Paperen_US
uow.relation.series95/10


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