A New Genetic Algorithm-Based Pruning Approach for Optimum-Path Forest

Abstract

Optimum-Path Forest (OPF) is a graph-based supervised classifier that has achieved remarkable performances in many applications. OPF has many advantages when compared to other supervised classifiers, since it is free of parameters, achieves zero classification errors on the training set without overfitting, handles multiple classes without modifications or extensions, and does not make assumptions about the shape and separability of the classes. However, one drawback of the OPF classifier is the fact that its classification computational cost grows proportionally to the size of the training set. To overcome this issue, we propose a novel method based on genetic algorithms (GAs) to prune irrelevant training samples and still preserve or even improve accuracy in OPF classification. We validate the method using public datasets obtained from UCI repository.