Approximate Versus Linguistic Representation in Fuzzy-UCS
نویسندگان
چکیده
This paper introduces an approximate fuzzy representation to FuzzyUCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies whether the flexibility provided by the approximate representation results in a significant improvement of the accuracy of the models evolved by the system. We test Fuzzy-UCS with both approximate and linguistic representation on a large collection of real-life problems and compare the results in terms of training and test accuracy and interpretability of the
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