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Incremental rule learning based on example nearness from numerical data streams

 

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Opened Access Incremental rule learning based on example nearness 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: SAC '05 Proceedings of the 2005 ACM symposium on Applied computing, pp. 568-572 (2005)
Document type: Chapter of Book
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.
Size: 176.0Kb
Format: PDF

URI: http://hdl.handle.net/11441/39713

DOI: http://dx.doi.org/10.1145/1066677.1066808

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