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Capítulo de Libro
Data streams classification by incremental rule learning with parameterized generalization
dc.creator | Ferrer Troyano, Francisco Javier | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-04-07T11:24:52Z | |
dc.date.available | 2016-04-07T11:24:52Z | |
dc.date.issued | 2006 | |
dc.identifier.uri | http://hdl.handle.net/11441/39729 | |
dc.description.abstract | Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up--to--date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbor algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another. | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.relation.ispartof | SAC '06 Proceedings of the 2006 ACM symposium on Applied computing, pp. 657-661 (2006) | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine learning | es |
dc.subject | Learning paradigms | es |
dc.subject | Classification and regression trees | es |
dc.title | Data streams classification by incremental rule learning with parameterized generalization | es |
dc.type | info:eu-repo/semantics/bookPart | es |
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/10.1145/1141277.1141428 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/39729 |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Data streams.pdf | 171.1Kb | [PDF] | Ver/ | |