A new variant of the optimum-path forest classifier

Abstract

We have shown a supervised approach for pattern classification, which interprets the training samples as nodes of a complete arc- weighted graph and computes an optimum-path forest rooted at some of the closest samples between distinct classes. A new sample is classified by the label of the root which offers to it the optimum path. We propose a variant, in which the training samples are the nodes of a graph, whose the arcs are the k-nearest neighbors in the feature space. The graph is weighted on the nodes by their probability density values (pdf) and the optimum-path forest is rooted at the maxima of the pdf. The best value of k is computed by the maximum accuracy of classification in the training set. A test sample is assigned to the class of the maximum, which offers to it the optimum path. Preliminary results have shown that the proposed approach can outperform the previous one and the SVM classifier in some datasets. © Springer-Verlag Berlin Heidelberg 2008.