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In the context of some machine learning applications, obtaining datainstances is a relatively easy process but labeling them could become quiteexpensive or tedious. Such scenarios lead to datasets with few labeledinstances and a larger number of unlabeled ones. Semi-supervised classificationtechniques combine labeled and unlabeled data during the learning phase inorder to increase the classifier's generalization capability. Regrettably, mostsuccessful semi-supervised classifiers do not allow explaining their outcome,thus behaving like black boxes. However, there is an increasing number ofproblem domains in which experts demand a clear understanding of the decisionprocess. In this paper, we report on an extended experimental study presentingan interpretable self-labeling grey-box classifier that uses a black box toestimate the missing class labels and a white box to explain the finalpredictions. Two different approaches for amending the self-labeling processare explored: a first one based on the confidence of the black box and thelatter one based on measures from Rough Set Theory. The results of the extendedexperimental study support the interpretability by means of transparency andsimplicity of our classifier, while attaining superior prediction rates whencompared with state-of-the-art self-labeling classifiers reported in theliterature.

An interpretable semi-supervised classifier using two different strategies for amended self-labeling