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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 | http://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 |