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dc.contributor.authorHolmes, Geoffrey
dc.date.accessioned2008-10-20T03:44:52Z
dc.date.available2008-10-20T03:44:52Z
dc.date.issued1997-04
dc.identifier.citationHolmes, G. (1997). Discovering inter-attribute relationships. (Working paper 97/13). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1076
dc.description.abstractIt is important to discover relationships between attributes being used to predict a class attribute in supervised learning situations for two reasons. First, any such relationship will be potentially interesting to the provider of a dataset in its own right. Second, it would simplify a learning algorithm’s search space, and the related irrelevant feature and subset selection problem, if the relationships were removed from datasets ahead of learning. An algorithm to discover such relationships is presented in this paper. The algorithm is described and a surprising number of inter-attribute relationships are discovered in datasets from the University of California at Irvine (UCI) repository.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.titleDiscovering inter-attribute relationshipsen_US
dc.typeWorking Paperen_US
uow.relation.series97/13


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