InstancRank based on borders for instance selection

dc.creatorPablo Francisco Hernández Leal
dc.creatorJesús Ariel Carrasco Ochoa
dc.creatorJosé Francisco Martínez Trinidad
dc.creatorJosé Arturo Olvera López
dc.date2013
dc.date.accessioned2023-07-25T16:25:18Z
dc.date.available2023-07-25T16:25:18Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2275
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7807455
dc.descriptionInstance selection algorithms are used for reducing the number of training instances. However, most of them suffer from long runtimes which results in the incapability to be used with large datasets. In this work, we introduce an Instance Ranking per class using Borders (instances near to instances belonging to different classes), using this ranking we propose an instance selection algorithm (IRB). We evaluated the proposed algorithm using k-NN with small and large datasets, comparing it against state of the art instance selection algorithms. In our experiments, for large datasets IRB has the best compromise between time and accuracy. We also tested our algorithm using SVM, LWLR and C4.5 classifiers, in all cases the selection computed by our algorithm obtained the best accuracies in average.
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier Ltd.
dc.relationcitation:Hernández-Leal, P., et al., (2013). InstanceRank based on borders for instance selection, Pattern Recognition, (46): 365-375
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.titleInstancRank based on borders for instance selection
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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