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Incremental Rule Learning and Border Examples Selection from Numerical Data Streams

 

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Opened Access Incremental Rule Learning and Border Examples Selection from Numerical Data Streams
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Author: Ferrer Troyano, Francisco J.
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2005
Published in: Journal of Universal Computer Science, 11 (8), 1426-1439.
Document type: Article
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 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.
Cite: 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.
Size: 133.4Kb
Format: PDF

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

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

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