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Discovering decision rules from numerical data streams

Opened Access Discovering decision rules 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: 2004
Publicado en: SAC '04 Proceedings of the 2004 ACM symposium on Applied computing, pp. 649-653 (2004)
Tipo de documento: Capítulo de Libro
Resumen: This paper presents a scalable learning algorithm to classify numerical, low dimensionality, high-cardinality, time-changing data streams. Our approach, named SCALLOP, provides a set of decision rules on demand which improves its simplicity and helpfulness for the user. SCALLOP updates the knowledge model every time a new example is read, adding interesting rules and removing out-of-date rules. As the model is dynamic, it maintains the tendency of data. Experimental results with synthetic data streams show a good performance with respect to running time, accuracy and simplicity of the model.
Tamaño: 146.1Kb
Formato: PDF

URI: http://hdl.handle.net/11441/39691

DOI: http://dx.doi.org/10.1145/967900.968036

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