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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amo, A., Montero, J., Biging, G., Cutello, V.: Fuzzy classification systems. European Journal of Operational Research 156(2), 495–507 (2004)
Atanassov, K.T.: Intuitionistic Fuzzy Sets. Physica-Verlag, Heidelberg (1999)
Destercke, S., Guillaume, S., Charnomordic, B.: Building an interpretable fuzzy rule base from data using Orthogonal Least Squares. Application to a depollution problem. Fuzzy Sets and Systems 158(18), 2078–2094 (2007)
Fortemps, P., Slowinski, R.: A graded quadrivalent logic for ordinal preference modelling: Loyola-like approach. Fuzzy Optimization and Decision Making 1(1), 93–111 (2002)
Fortemps, P., Greco, S., Slowinski, R.: Multicriteria decision support using rules that represent rough-graded preference relations. European J. Operational Research 188(1), 206–223 (2008)
Hammer, P.L., Bonates, T.: Logical Analysis of Data - An overview: From Combinatorial Optimization to Medical Applications. Annals of Operations Research 148(1), 203–225 (2006)
Iliadis, L.S.: A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental Modelling & Software 20, 613–621 (2005)
Mamdani, E.H.: Application of Fuzzy Algorithms for the Control of a Dynamic Plant. Proc. IEE 121(12), 1585–1588 (1974)
Montero, J., Gómez, D., Bustince, H.: On the relevance of some families of fuzzy sets. Fuzzy sets and systems 158(22), 2439–2442 (2007)
Novak, V.: Antonyms and linguistic quantifiers in fuzzy logic. Fuzzy Sets and Systems 124, 335–351 (2001)
Öztürk, M., Tsoukiàs, A.: Modelling uncertain positive and negative reasons in decision aiding. Decision Support Systems 43(4), 1512–1526 (2007)
Paradis, C., Willners, C.: Antonymy and negation—the boundness hypothesis. J. Pragmatics 38, 1051–1080 (2006)
Rodriguez, J.T., Vitoriano, B., Montero, J., Omaña, A.: A decision support tool for humanitarian organizations in natural disaster relief. In: Ruan, D., et al. (eds.) Computational Intelligence in Decision and Control, pp. 600–605. World Scientific, Singapore (2008)
Ruspini, E.H.: A new approach to clustering. Inform. Control 15, 22–32 (1969)
Tsoukiàs, A.: A first-order, four valued, weakly paraconsistent logic and its relation to rough sets semantics. Foundations of Computing and Decision Sciences 12, 85–108 (2002)
Yager, R.R.: Targeted e-commerce marketing using fuzzy intelligent agents. Intelligent Systems and their Applications 15(6), 42–45 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rodríguez, J.T., Montero, J., Vitoriano, B., López, V. (2009). An Inductive Methodology for Data-Based Rules Building. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-04428-1_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04427-4
Online ISBN: 978-3-642-04428-1
eBook Packages: Computer ScienceComputer Science (R0)