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dc.contributor.authorSmith, Tony C.
dc.contributor.authorHolmes, Geoffrey
dc.date.accessioned2008-10-20T22:59:08Z
dc.date.available2008-10-20T22:59:08Z
dc.date.issued1995-04
dc.identifier.citationSmith, T. C. & Holmes, G. (1995). Subset selection using rough numeric dependency. (Working paper 95/12). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1090
dc.description.abstractIn this paper we describe a novel method for performing feature subset selection for supervised learning tasks based on a refined notion of feature relevance. We define relevance as others see it and outline our refinement of this concept. We then describe how we use this new definition in an algorithm to perform subset selection, and finally, we show some preliminary results of using this approach with two quite different supervised learning schemes.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.subjectfilter modelen_US
dc.subjectrelevanceen_US
dc.subjectmachine learningen_US
dc.subjectnumeric dependencyen_US
dc.titleSubset selection using rough numeric dependencyen_US
dc.typeWorking Paperen_US
uow.relation.series95/12


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