Optimum Path Forest

Optimum-Path Forest (OPF) is a graph-based machine learning classifier that uses a novel approach to build decision trees and forests for classification tasks. Current research focuses on improving OPF's performance, particularly by exploring different distance metrics for constructing the graph and developing strategies to handle imbalanced datasets, including oversampling and undersampling techniques. This parameterless approach offers a computationally efficient alternative to traditional classifiers, showing promise in various applications and driving ongoing investigations into its robustness and adaptability across diverse data types.