A review of instance selection methods

dc.creatorJOSE ARTURO OLVERA LOPEZ
dc.creatorJESUS ARIEL CARRASCO OCHOA
dc.creatorJOSE FRANCISCO MARTINEZ TRINIDAD
dc.creatorJosef Kittler
dc.date2010
dc.date.accessioned2023-07-25T16:23:34Z
dc.date.available2023-07-25T16:23:34Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1389
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7806585
dc.descriptionIn supervised learning, a training set providing previously known information is used to classify new instances. Commonly, several instances are stored in the training set but some of them are not useful for classifying therefore it is possible to get acceptable classification rates ignoring non useful cases; this process is known as instance selection. Through instance selection the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers. This work is focused on presenting a survey of the main instance selection methods reported in the literature.
dc.formatapplication/pdf
dc.languageeng
dc.publisherSpringer Science+Business Media B.V.
dc.relationcitation:Olvera-López, J.A., et al., (2010). A review of instance selection methods, Artificial Intelligence Review. (34):133–143
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleA review of instance selection methods
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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