The impact of reliability evaluation on a semi-supervised learning approach

Abstract

In self-training methods, unlabeled samples are first assigned a provisional label by the classifier, and then used to extend the training set of the classifier itself. For this latter step it is important to choose only the samples whose classification is likely to be correct, according to a suitably defined reliability measure. In this paper we want to study to what extent the choice of a particular technique for evaluating the classification reliability can affect the learning performance. To this aim, we have compared five different reliability evaluators on four publicly available datasets, analyzing and discussing the obtained results. © 2009 Springer Berlin Heidelberg.