An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling

Semi-supervised classifiers combine labeled and unlabeled
data during the learning phase in order to increase
classifier’s generalization capability. However, most successful
semi-supervised classifiers involve complex ensemble structures
and iterative algorithms which make it difficult to explain the
outcome, thus behaving like black boxes. Furthermore, during
an iterative self-labeling process, mistakes can be propagated if
no amending procedure is used. In this paper, we build upon an
interpretable self-labeling grey-box classifier that uses a black
box to estimate the missing class labels and a white box to make
the final predictions. We propose a Rough Set based approach for
amending the self-labeling process. We compare its performance
to the vanilla version of our self-labeling grey-box and the
use of a confidence-based amending. In addition, we introduce
some measures to quantify the interpretability of our model.
The experimental results suggest that the proposed amending
improves accuracy and interpretability of the self-labeling grey-box,
thus leading to superior results when compared to state-of-the-
art semi-supervised classifiers.