OPFython: A Python-Inspired Optimum-Path Forest Classifier
This provides a Python library for researchers and practitioners to more easily apply the Optimum-Path Forest classifier, but it is incremental as it ports an existing method to a new language.
The paper introduces OPFython, a Python-based framework for the Optimum-Path Forest classifier, which is a state-of-the-art graph-inspired technique comparable to Support Vector Machines, offering a more user-friendly environment and faster prototyping than the original C implementation.
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, and Bayesian classifiers, might lack complexity and diversity, not suitable when dealing with real-world data. A recent graph-inspired classifier, known as the Optimum-Path Forest, has proven to be a state-of-the-art technique, comparable to Support Vector Machines and even surpassing it in some tasks. This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython, where all of its functions and classes are based upon the original C language implementation. Additionally, as OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language.