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dc.date.accessioned 2012-11-14T14:04:57Z
dc.date.available 2012-11-14T14:04:57Z
dc.date.issued 2006-08
dc.identifier.uri https://sedici.unlp.edu.ar/handle/10915/24150
dc.description.abstract Rough Set Theory (RST) is a technique for data analysis. In this study, we use RST to improve the performance of k-NN method. The RST is used to edit and reduce the training set. We propose two methods to edit training sets, which are based on the lower and upper approximations. Experimental results show a satisfactory performance of k-NN method using these techniques. en
dc.language en es
dc.subject data analysis en
dc.subject training sets en
dc.subject Rough Set Theory (RST) en
dc.title Improving the k-NN method: rough set in edit training set en
dc.type Objeto de conferencia es
sedici.identifier.isbn 0-387-34655-4 es
sedici.creator.person Caballero, Yailé es
sedici.creator.person Bello, Rafael es
sedici.creator.person Álvarez, Delia es
sedici.creator.person García, María M. es
sedici.creator.person Pizano, Yaimara es
sedici.description.note Applications in Artificial Intelligence - Learning and Neural Nets es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2006-08
sedici.relation.event 19 th IFIP World Computer Congress - WCC 2006 es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)