Interpretable self-labeling semi-supervised classifier

Semi-supervised classification refers to a type ofpattern classification problem involving both labeledand unlabeled data, where the number of labeledinstances is often significantly smaller comparedto the number of unlabeled ones. Althoughthere exist several semi-supervised classifiers withhigh performance over different tasks, most of themare complex models that do not allow explainingthe obtained outcome, thus behaving like blackboxes. In this paper, we perform a critical analysisof the interpretability of state-of-the-art semisupervisedclassification approaches. In addition,we present a self-labeling grey-box classifier thatuses a black-box to estimate the missing class labelsand an interpretable white-box to make theactual predictions. The main contribution of thismodel relies on its transparency while also beingable to outperform most state-of-the-art semisupervisedclassifiers.