On the Training Patterns Pruning for Optimum-Path Forest

The Optimum-Path Forest (OPF) classifier is a novel graph-based supervised pattern recognition technique that has been demonstrated to be superior to Artificial Neural Networks and similar to Support Vector Machines, but much faster. The OPF classifier reduces the problem of pattern recognition to a computation of an optimum-path forest in the feature space induced by a graph, creating discrete optimal partitions, which are optimum-path trees rooted by prototypes, i.e., key samples that will compete among themselves trying to conquer the remaining samples. Some applications, such that medical specialist systems for image-based diseases identification, need to be constantly re-trained with new instances (diagnostics) to achieve a better generalization of the problem, which requires large storage devices, due to the high number of generated data (millions of voxels). In that way, we present here a pruning algorithm for the OPF classifier that learns the most irrelevant samples and eliminate them from the training set, without compromising the classifier’s accuracy.