Isel Grau, Dipankar Sengupta, Maria M. Garcia Lorenzo, Ann Nowe
Semi-supervised classifiers combine labeled and unlabeled data during the learning phase in order to increase classifier{\textquoteright}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.
Grau, I, Sengupta, D, Garcia Lorenzo, MM & Nowe, A 2020, An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling. in 2020 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2020 - Proceedings. IEEE, pp. 1-8, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/FUZZ48607.2020.9177549
Grau, I., Sengupta, D., Garcia Lorenzo, M. M., & Nowe, A. (2020). An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling. In 2020 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2020 - Proceedings (pp. 1-8). IEEE. https://doi.org/10.1109/FUZZ48607.2020.9177549
@inproceedings{2d295fd54f834e3185929b66f41f131f,
title = "An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling",
abstract = "Semi-supervised classifiers combine labeled and unlabeled data during the learning phase in order to increase classifier{\textquoteright}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.",
author = "Isel Grau and Dipankar Sengupta and \{Garcia Lorenzo\}, \{Maria M.\} and Ann Nowe",
year = "2020",
doi = "10.1109/FUZZ48607.2020.9177549",
language = "English",
isbn = "978-1-7281-6933-0",
pages = "1--8",
booktitle = "2020 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2020 - Proceedings",
publisher = "IEEE",
note = " IEEE World Congress on Computational Intelligence (WCCI) 2020 : IEEE International Conference on Fuzzy Systems, FUZZ-IEEE ; Conference date: 19-07-2020",
url = "https://wcci2020.org/",
}