New trends in the fuzzy modeling part I: novel approaches

Nowadays, one of the most important areas of application of fuzzy set theory are fuzzy rule-based systems. These kinds of systems constitute an extension of classical rule-based systems, because they deal with “IF-THEN” rules whose antecedents and consequents are composed of fuzzy logic statements instead of classical logic. They have been successfully applied to a wide range of problems in different domains for which uncertainty and vagueness emerge in different ways. The current special issues on “New trends in fuzzy modeling (Part I: Novel approaches and Part II: Applications) collect several contributions by experts in the topic that go beyond on the design of fuzzy systems with a balance between new developments and applications. This first issue is devoted to present new trends on the design of fuzzy systems. The papers in this issue can be classified according to different paradigms and new trends in the design of fuzzy systems. We have divided them into three groups, the first one devoted to the trade-off between interpretability and precision, the second group focused on the input selection and fuzzy systems learning, and the last one including papers dealing with different new trends in the design of fuzzy systems, as the integration of boosting methods with fuzzy rules learning, a novel classification of data-driven approaches for designing fuzzy systems made on the basis of the optimisation techniques used for this purpose, and the use of default reasoning with specific rules for designing a genetics based machine learning algorithm. The first five papers in this issue belong to the first group devoted to the trade-off between interpretability and precision. In the first paper within this group, entitled “Hybrid learning models to get the interpretability-accuracy trade-off in