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An Inductive Methodology for Data-Based Rules Building

Tinguaro Rodríguez, Juan and Montero de Juan, Francisco Javier and Vitoriano Villanueva, Begoña and 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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Abstract

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.

Item Type:Book Section
Additional Information:Lecture Notes in Artificial Intelligence
Uncontrolled Keywords:Rules induction; DDT logic; Fuzzy inference systems; Dual predicates
Subjects:Sciences > Mathematics > Operations research
ID Code:16858
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Deposited On:25 Oct 2012 08:54
Last Modified:07 Feb 2014 09:37

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