Fuzzy modeling has become one of the most productive and
successful results of fuzzy logic. Among others, it has been
applied to knowledge discovery, automatic classification, long-term
prediction, or medical and engineering analysis. The research
developed in the topic during the last two decades has been mainly
focused on exploiting the fuzzy model flexibility to obtain the
highest accuracy. This approach usually sets aside the
interpretability of the obtained models. However, we should
remember the initial philosophy of fuzzy sets theory directed to
serve the bridge between the human understanding and the machine
processing. In this challenge, the ability of fuzzy models to
express the behavior of the real system in a comprehensible manner
acquires a great importance. This book collects the works of a
group of experts in the field that advocate the interpretability
improvements as a mechanism to obtain well balanced fuzzy
models.
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