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Incremental Rule Learning and Border Examples Selection from Numerical Data Streams

 

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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: 2005
Publicado en: Journal of Universal Computer Science, 11 (8), 1426-1439.
Tipo de documento: Artículo
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 neighbour 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.
Cita: Ferrer Troyano, F.J., Aguilar Ruiz, J.S. y Riquelme Santos, J.C. (2005). Incremental Rule Learning and Border Examples Selection from Numerical Data Streams. Journal of Universal Computer Science, 11 (8), 1426-1439.
Tamaño: 133.4Kb
Formato: PDF

URI: http://hdl.handle.net/11441/43230

DOI: http://dx.doi.org/10.3217/jucs-011-08-1426

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