  In the context of some machine learning applications, obtaining data
instances is a relatively easy process but labeling them could become quite
expensive or tedious. Such scenarios lead to datasets with few labeled
instances and a larger number of unlabeled ones. Semi-supervised classification
techniques combine labeled and unlabeled data during the learning phase in
order to increase the classifier's generalization capability. Regrettably, most
successful semi-supervised classifiers do not allow explaining their outcome,
thus behaving like black boxes. However, there is an increasing number of
problem domains in which experts demand a clear understanding of the decision
process. In this paper, we report on an extended experimental study presenting
an interpretable self-labeling grey-box classifier that uses a black box to
estimate the missing class labels and a white box to explain the final
predictions. Two different approaches for amending the self-labeling process
are explored: a first one based on the confidence of the black box and the
latter one based on measures from Rough Set Theory. The results of the extended
experimental study support the interpretability by means of transparency and
simplicity of our classifier, while attaining superior prediction rates when
compared with state-of-the-art self-labeling classifiers reported in the
literature.
