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This paper briefly elaborates on a development in (applied) fuzzy logic thathas taken place in the last couple of decades, namely, the complementation oreven replacement of the traditional knowledge-based approach to fuzzyrule-based systems design by a data-driven one. It is argued that the classicalrule-based modeling paradigm is actually more amenable to the knowledge-basedapproach, for which it has originally been conceived, while being less apt todata-driven model design. An important reason that prevents fuzzy (rule-based)systems from being leveraged in large-scale applications is the flat structureof rule bases, along with the local nature of fuzzy rules and their limitedability to express complex dependencies between variables. This motivatesalternative approaches to fuzzy systems modeling, in which functionaldependencies can be represented more flexibly and more compactly in terms ofhierarchical structures.

From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection