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dc.contributor.authorGüvenir, H. Altayen_US
dc.coverage.spatialSozopol, Bulgaria
dc.date.accessioned2016-02-08T11:58:50Z
dc.date.available2016-02-08T11:58:50Z
dc.date.issued1998-09en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27656
dc.descriptionDate of Conference: 21-23 September, 1998
dc.descriptionConference name: 8th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA’98
dc.description.abstractPresence of irrelevant features is a fact of life in many realworld applications of classification learning. Although nearest-neighbor classification algorithms have emerged as a promising approach to machine learning tasks with their high predictive accuracy, they are adversely affected by the presence of such irrelevant features. In this paper, we describe a recently proposed classification algorithm called VFI5, which achieves comparable accuracy to nearest-neighbor classifiers while it is robust with respect to irrelevant features. The paper compares both the nearest-neighbor classifier and the VFI5 algorithms in the presence of irrelevant features on both artificially generated and real-world data sets selected from the UCI repository.en_US
dc.language.isoEnglishen_US
dc.source.titleAIMSA 1998: International Conference on Artificial Intelligence: Methodology, Systems, and Applicationsen_US
dc.relation.isversionofhttps://doi.org/10.1007/BFb0057452
dc.subjectClassification algorithmen_US
dc.subjectClassification learningen_US
dc.subjectNearest neighbor classifiersen_US
dc.subjectNearest-neighborsen_US
dc.subjectPredictive accuracyen_US
dc.titleA classification learning algorithm robust to irrelevant featuresen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage281en_US
dc.citation.epage290en_US
dc.identifier.doi10.1007/BFb0057452
dc.publisherSpringeren_US


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