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

Isel Grau, Dipankar Sengupta, Maria M. Garcia Lorenzo, Ann Nowé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE International Conference on Fuzzy Systems
Publisher IEEE
Number of pages9
DOIs
Publication statusPublished - 26 Aug 2020
EventIEEE World Congress on Computational Intelligence 2020: IEEE International Conference on Fuzzy Systems - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameIEEE International Conference on Fuzzy Systems (FUZZ-IEEE): Proceedings
PublisherIEEE
ISSN (Print)1544-5615
ISSN (Electronic)1558-4739

Conference

ConferenceIEEE World Congress on Computational Intelligence 2020
Abbreviated titleIEEE WCCI - FUZZ IEEE 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/202024/07/2020

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    Grau, I., Sengupta, D., Lorenzo, M. M. G., & Nowé, A. (2020). An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling. In Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): Proceedings). IEEE . https://doi.org/10.1109/FUZZ48607.2020.9177549