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dc.creatorFerrer Troyano, Francisco Javieres
dc.creatorAguilar Ruiz, Jesús Salvadores
dc.creatorRiquelme Santos, José Cristóbales
dc.date.accessioned2016-04-07T09:50:33Z
dc.date.available2016-04-07T09:50:33Z
dc.date.issued2004
dc.identifier.urihttp://hdl.handle.net/11441/39691
dc.description.abstractThis paper presents a scalable learning algorithm to classify numerical, low dimensionality, high-cardinality, time-changing data streams. Our approach, named SCALLOP, provides a set of decision rules on demand which improves its simplicity and helpfulness for the user. SCALLOP updates the knowledge model every time a new example is read, adding interesting rules and removing out-of-date rules. As the model is dynamic, it maintains the tendency of data. Experimental results with synthetic data streams show a good performance with respect to running time, accuracy and simplicity of the model.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.relation.ispartofSAC '04 Proceedings of the 2004 ACM symposium on Applied computing, pp. 649-653 (2004)es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleDiscovering decision rules from numerical data streamses
dc.typeinfo:eu-repo/semantics/bookPartes
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.identifier.doihttp://dx.doi.org/10.1145/967900.968036es
dc.identifier.idushttps://idus.us.es/xmlui/handle/11441/39691

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