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dc.contributor.authorHowe, Nicholasen_US
dc.contributor.authorCardie, Claireen_US
dc.date.accessioned2007-04-23T18:16:35Z
dc.date.available2007-04-23T18:16:35Z
dc.date.issued1999-03en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR99-1735en_US
dc.identifier.urihttps://hdl.handle.net/1813/7389
dc.description.abstractFeature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting algorithm that works with any kind of feature for which a distance function can be defined. Applied to an image classification task with unusual set-like features, the technique improves classification accuracy significantly. In tests on standard data sets from the UCI repository, the technique yields improvements comparable to weighting features by information gain.en_US
dc.format.extent126417 bytes
dc.format.extent100409 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleWeighting Unusual Feature Typesen_US
dc.typetechnical reporten_US


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