Supervised Learning Using Local Analysis in an Optimal-Path Forest

In this paper, we present an OPF-LA (Optimal Path Forest -- Local Analysis), a new learning model proposal. OPF-LA is a heuristic that uses local information for selecting prototypes that, in turn, will be used to classify new data. It employs the main ideas of an OPF classifier, suggesting a new procedure in the data training phase. Experimental results show the advantages in efficiency and accuracy over classical learning algorithms in areas such as Support Vector Machines (SVM), Artificial Neural Networks using Multilayer Perceptrons (MP), and Optimal Path Forest (OPF), in several applications.