Theoretical background and related works

Abstract

The Optimum-Path Forest (OPF) is a framework for the design of graph-based classifiers, which covers supervised, semisupervised, and unsupervised applications. The OPF is mainly characterized by its low training and classification times as well as competitive results against well-established machine learning techniques, such as Support Vector Machine and Artificial Neural Networks. Besides, the framework allows the design of different approaches based on the problem itself, which means a specific OPF-based classifier can be built for a given particular task. This paper surveyed several works published in the past years concerning OPF-based classifiers and sheds light on future trends concerning such a framework in the context of the deep learning era. © 2022 Copyright