Tree-Oriented Hypothesis Generation for Interpretable Fuzzy Rules
نویسندگان
چکیده
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.
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