Authors:
F. Benmakrouha
;
C. Hespel
;
E. Monnier
and
D. Quichaud
Affiliation:
Computer Sciences INSA, France
Keyword(s):
Fuzzy System, Diabetes, Datum Plane Covering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Information Retrieval and Data Mining
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Soft Computing
Abstract:
The aim of this paper is to propose a criterion to estimate the design, from experimental data, of a fuzzy inference system, when data are sparse. This lack of data is important and may improve the generalisation ability of fuzzy systems (Isao Ishibuchi, 2002).
Several methods have been proposed to obtain automatic fuzzy rules from sparse training data. In (Cruz Vega Israel, 2010), the authors first construct fuzzy rules from collect data. Then, they use kernel regressions for generate training data.
Another technique used when classical inference methods produce sparse fuzzy rules is a diffusion procedure based on interpolation to initialize incomplete rules (Benmakrouha, 1997), (Glorennec, 1999), (Baranyi, 1996). Our method has the advantage of occuring before initialization step and therefore avoiding unfired rules which make difficult to produce an accurate output.