One-class to multi-class model update using the class-incremental optimum-path forest classifier

Abstract

Incremental learning capabilities of classifiers is a relevant topic, specially when dealing with scenarios such as data stream mining, concept drift and active learning. We investigate the capabilities of an incremental version of the Optimum-Path Forest classifier (OPF-CI) in the context of learning new classes and compare its behavior against Support Vector Machines and k Nearest Neighbours classifiers. The OPF-CI classifier is a parameter-free, graphbased approach to incremental training that runs in linear time with respect to the number of instances. Our results show OPF-CI keeps the running time low while producing an accuracy behavior similar to the other classifiers for increments of instances. Also, it is robust to variations on the order of the learned classes, demonstrating the applicability of the method.