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Data streams classification by incremental rule learning with parameterized generalization

Opened Access Data streams classification by incremental rule learning with parameterized generalization

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Autor: Ferrer Troyano, Francisco J.
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2006
Publicado en: SAC '06 Proceedings of the 2006 ACM symposium on Applied computing, pp. 657-661 (2006)
Tipo de documento: Capítulo de Libro
Resumen: 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.
Tamaño: 171.1Kb
Formato: PDF

URI: http://hdl.handle.net/11441/39729

DOI: http://dx.doi.org/10.1145/1141277.1141428

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