RT Book, Section
T1 An Inductive Methodology for Data-Based Rules Building
A1 Rodríguez González, Juan Tinguaro
A1 Montero De Juan, Francisco Javier
A1 Vitoriano Villanueva, Begoña
A1 López, Victoria
A2 Rossi, Francesca
A2 Tsoukias, Alexis
AB 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.
PB Springer-Verlag Berlin Heidelberg
SN 978-3-642-04427-4
YR 2009
FD 2009
LK https://hdl.handle.net/20.500.14352/53154
UL https://hdl.handle.net/20.500.14352/53154
LA eng
NO Rodríguez, J.T., Montero, J., Vitoriano, B., López, V.: An Inductive Methodology for Data-Based Rules Building. En: Rossi, F. y Tsoukias, A. (eds.) Algorithmic Decision Theory. pp. 424-433. Springer Berlin Heidelberg, Berlin, Heidelberg (2009)
NO Lecture Notes in Artificial Intelligence
DS Docta Complutense
RD 15 abr 2025