Tree-oriented hypothesis generation for interpretable fuzzy rules
Jens Jäkel, Lutz Gröll, Ralf Mikut · 1999
The paper presents a new approach to the automatic data-based generation of fuzzy rules. This is based on a tree-oriented rule induction algorithm and rule pruning. The hypothesis generation applies a set of measures for evaluation of fuzzy rules with respect to approximation quality, importance, clearness etc. In order to improve #exibility and interpretability linguistic hedges are used to create derived linguistic terms.