Show simple item record  

dc.contributor.authorHall, Mark A.
dc.date.accessioned2008-10-17T03:04:36Z
dc.date.available2008-10-17T03:04:36Z
dc.date.issued1999-04
dc.identifier.citationHall, M.A. (1999). Feature selection for discrete and numeric class machine learning. (Working paper 99/04). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1033
dc.description.abstractAlgorithms for feature selection fall into two broad categories: wrappers use the learning algorithm itself to evaluate the usefulness of features, while filters evaluate features according to heuristics based on general characteristics of the data. For application to large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filter algorithms only work with discrete classification problems. This paper describes a fast, correlation-based filter algorithm that can be applied to continuous and discrete problems. Experiments using the new method as a preprocessing step for naïve Bayes, instance-based learning, decision trees, locally weighted regression, and model trees show it to be an effective feature selector - it reduces the data in dimensionality by more than sixty percent in most cases without negatively affecting accuracy. Also, decision and model trees built from the pre-processed data are often significantly smaller.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherComputer Science, University of Waikatoen_NZ
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.titleFeature selection for discrete and numeric class machine learningen_US
dc.typeWorking Paperen_US
uow.relation.series99/04
pubs.elements-id54936
pubs.place-of-publicationHamiltonen_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record