In this paper, we propose a novel and interpretable grey-box ensemble using a self-labeled approach for semi-supervised classification problems. The prospective grey-box ensembles a more interpretable white-box model with a black-box technique. This scheme could guide the comparatively data expensive white-box component with the results from the more accurate black-box part. We evaluate the proposal in an inductive learning setting showing good performance in partially labeled datasets.