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
| Title | An interpretable semi-supervised classifier using rough sets for amended self-labeling |
|---|---|
| Authors | Grau, I., Sengupta, D., Garcia Lorenzo, M.M. and Nowe, A. |
| Type | Conference 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. |
| Year | 2020 |
| Conference | IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020) |
| Publisher | IEEE |
| Publication dates | |
| Published | 26 Aug 2020 |
| ISBN | 9781728169323 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/fuzz48607.2020.9177549 |