  In this article, a new Fuzzy Granular Approximation Classifier (FGAC) is
introduced. The classifier is based on the previously introduced concept of the
granular approximation and its multi-class classification case. The classifier
is instance-based and its biggest advantage is its local transparency i.e., the
ability to explain every individual prediction it makes. We first develop the
FGAC for the binary classification case and the multi-class classification case
and we discuss its variation that includes the Ordered Weighted Average (OWA)
operators. Those variations of the FGAC are then empirically compared with
other locally transparent ML methods. At the end, we discuss the transparency
of the FGAC and its advantage over other locally transparent methods. We
conclude that while the FGAC has similar predictive performance to other
locally transparent ML models, its transparency can be superior in certain
cases.
