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An Inductive Methodology for Data-Based Rules Building

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Rodríguez, Juan Tinguaro y Montero, Javier y Vitoriano, Begoña y Lopez, Victoria (2009) An Inductive Methodology for Data-Based Rules Building. In Algorithmic Decision Theory: First International Conference, ADT 2009, Venice, Italy, October 2009, Proceedings. Lecture Notes in Computer Science (5783). Springer-Verlag Berlin Heidelberg, Berlin, pp. 424-433. ISBN 978-3-642-04427-4

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URL Oficial: http://www.springerlink.com/content/b650283049324378/fulltext.pdf


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Extraction of rules from databases for classification and decision tasks is an issue of growing importance as automated processes based on data are being required in these fields. An inductive methodology for data-based rules building and automated learning is presented in this paper. A fuzzy framework is used for knowledge representation and, through the introduction and the use of dual properties in the valuation space of response variables, reasons for and against the rules are evaluated from data. This make possible to use continuous DDT logic, which provides a more general and informative framework, in order to assess the validity of rules and build an appropriate knowledge base.


Tipo de documento:Sección de libro
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Lecture Notes in Artificial Intelligence

Palabras clave:Rules induction; DDT logic; Fuzzy inference systems; Dual predicates
Materias:Ciencias > Matemáticas > Investigación operativa
Código ID:16858
Depositado:25 Oct 2012 08:54
Última Modificación:25 May 2016 15:04

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