  In the past decades, fuzzy logic has played an essential role in many
research areas. Alongside, graph-based pattern recognition has shown to be of
great importance due to its flexibility in partitioning the feature space using
the background from graph theory. Some years ago, a new framework for both
supervised, semi-supervised, and unsupervised learning named Optimum-Path
Forest (OPF) was proposed with competitive results in several applications,
besides comprising a low computational burden. In this paper, we propose the
Fuzzy Optimum-Path Forest, an improved version of the standard OPF classifier
that learns the samples' membership in an unsupervised fashion, which are
further incorporated during supervised training. Such information is used to
identify the most relevant training samples, thus improving the classification
step. Experiments conducted over twelve public datasets highlight the
robustness of the proposed approach, which behaves similarly to standard OPF in
worst-case scenarios.
