  Machine Learning has attracted considerable attention throughout the past
decade due to its potential to solve far-reaching tasks, such as image
classification, object recognition, anomaly detection, and data forecasting. A
standard approach to tackle such applications is based on supervised learning,
which is assisted by large sets of labeled data and is conducted by the
so-called classifiers, such as Logistic Regression, Decision Trees, Random
Forests, and Support Vector Machines, among others. An alternative to
traditional classifiers is the parameterless Optimum-Path Forest (OPF), which
uses a graph-based methodology and a distance measure to create arcs between
nodes and hence sets of trees, responsible for conquering the nodes, defining
their labels, and shaping the forests. Nevertheless, its performance is
strongly associated with an appropriate distance measure, which may vary
according to the dataset's nature. Therefore, this work proposes a comparative
study over a wide range of distance measures applied to the supervised
Optimum-Path Forest classification. The experimental results are conducted
using well-known literature datasets and compared across benchmarking
classifiers, illustrating OPF's ability to adapt to distinct domains.
