Publication: A methodology for building fuzzy rules from data
Loading...
Official URL
Full text at PDC
Publication Date
2009
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Public University of Navarre
Citations
Exportar
Abstract
Extraction of rules for classification and decision tasks from databases is an issue of growing importance as automated processes based on data are being required in these fields. Interpretability of rules is improved by defining classes for independent variables. Moreover, though more complex, a more realistic and flexible framework is attained when fuzzy classes are considered. In this paper, an inductive approach is taken in order to develop a general methodology for building fuzzy rules from databases. Three types of rules are built in order to be able of dealing with both categorical and numerical data.
Description
EuroFuse Workshop, September 16-18, 2009, Pamplona
UCM subjects
Unesco subjects
Keywords
Citation
[1] A. Amo, J. Montero, G. Biging, V. Cutello (2004). Fuzzy classification systems European Journal of Operational Research 156 (2): 495-507
[2] S. Destercke, S. Guillaume, B. Charnomordic (2007). 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
[3] L.S. Iliadis (2005). A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation,Environmental Modelling & Software, 20 613-621
[4] E.H. Mamdani (1974). Application of Fuzzy Algorithms for the Control of a Dynamic Plant, Proc. IEE, 121 (12): 1585-1588
[5] J. Montero, D. Gómez, H. Bustince (2007). On the relevance of some families of fuzzy sets, Fuzzy sets and systems, 158 (22): 2439-2442
[6] J.T. Rodriguez, B. Vitoriano, J. Montero, A.Omaña (2008). A decision support tool for humanitarian organizations in natural disaster relief, in: D. Ruan et al. (eds), Computational Intelligence in Decision and
Control. World Scientific, Singapore: p.600-605
[7] E.H. Ruspini (1969). A new approach to clustering,Inform. Control 15 22–32.
[8] M. Öztürk, A. Tsoukiàs (2007). Modelling uncertain positive and negative reasons in decision aiding, Decision Support Systems,43 (4): 1512-1526
[9] R.R. Yager (2000). Targeted e-commerce marketing using fuzzy intelligent agents Intelligent Systems and their Applications,15 (6): 42-45