An interpretable semi-supervised classifier using rough sets for amended self-labeling

Grau, I., Sengupta, D., Garcia Lorenzo, M.M. and Nowe, A. 2020. An interpretable semi-supervised classifier using rough sets for amended self-labeling. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020). Glasgow, UK 19 - 24 Jul 2020 IEEE . https://doi.org/10.1109/fuzz48607.2020.9177549

TitleAn interpretable semi-supervised classifier using rough sets for amended self-labeling
AuthorsGrau, I., Sengupta, D., Garcia Lorenzo, M.M. and Nowe, A.
TypeConference paper
Abstract

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.

Year2020
ConferenceIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020)
PublisherIEEE
Publication dates
Published26 Aug 2020
ISBN9781728169323
Digital Object Identifier (DOI)https://doi.org/10.1109/fuzz48607.2020.9177549

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