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Capítulo de Libro
Mining Low Dimensionality Data Streams of Continuous Attributes
dc.creator | Ferrer Troyano, Francisco Javier | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-03-31T10:18:40Z | |
dc.date.available | 2016-03-31T10:18:40Z | |
dc.date.issued | 2003 | |
dc.identifier.uri | http://hdl.handle.net/11441/39235 | |
dc.description.abstract | This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high–cardinality, time–changing data streams. Within the Supervised Learning field, our approach, named SCALLOP, provides a set of decision rules whose size is very near to the number of concepts to be extracted. Experimental results with synthetic databases of different complexity degrees show a good performance from streams of data received at a rapid rate, whose label distribution may not be stationary in time. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.relation.ispartof | Progress in Artificial Intelligence, Lecture Notes in Computer Science, pp 264-278 (2003) | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Classification | es |
dc.subject | decision rules | es |
dc.subject | incremental learning | es |
dc.subject | scalable learning algorithms | es |
dc.subject | data streams | es |
dc.title | Mining Low Dimensionality Data Streams of Continuous Attributes | es |
dc.type | info:eu-repo/semantics/bookPart | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-540-24580-3_33 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/39235 |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Mining low.pdf | 580.6Kb | [PDF] | Ver/ | |