Active Semi-supervised Learning Using Optimum-Path Forest

The development of effective and efficient ways of handling real-world applications is becoming increasingly widespread, yet it still faces a number of practical challenges. First and foremost, we have the limited availability of labeled data in contrast to an unbounded number of unlabeled ones. Despite some efforts in active semi-supervised learning, their success depends on an approach suitable to be applied to real massive data. In this paper, we introduce a novel integration of semi-supervised learning and a priori-reduction and organization criteria for active learning based on Optimum-Path Forest classifiers. Encouraging results on both public and real data show the synergy of these strategies jointly. Our approach iteratively generates semi-supervised classifiers that attain high accuracy by selecting the most representative labeled set, while decreasing the propagated errors on the unlabeled set. In addition, it is able to identify samples from all classes quickly while keeping user interaction to a minimum throughout the learning iterations.