An Inductive Methodology for Data-Based Rules Building

Authors:
J. Tinguaro Rodríguez;Javier Montero;Begoña Vitoriano;Victoria López
Affiliations:
Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain 28040;Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain 28040;Faculty of Mathematics, Complutense University of Madrid, Madrid, Spain 28040;Faculty of Informatics, Complutense University of Madrid, Madrid, Spain 28040
Venue:
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Year:
2009

Citing 4
Cited 1

Targeted E-commerce Marketing Using Fuzzy Intelligent Agents

IEEE Intelligent Systems
Building an interpretable fuzzy rule base from data using Orthogonal Least Squares---Application to a depollution problem

Fuzzy Sets and Systems
Modelling uncertain positive and negative reasons in decision aiding

Decision Support Systems
On the relevance of some families of fuzzy sets

Fuzzy Sets and Systems

A disaster-severity assessment DSS comparative analysis

OR Spectrum

Quantified Score

Hi-index	0.00

Visualization

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.