Isel Grau, Dipankar Sengupta, Maria M. Garcia Lorenzo, Ann Nowe
Semi-supervised classifiers combine labeled and unlabeleddata during the learning phase in order to increaseclassifier{\textquoteright}s generalization capability. However, most successfulsemi-supervised classifiers involve complex ensemble structuresand iterative algorithms which make it difficult to explain theoutcome, thus behaving like black boxes. Furthermore, duringan iterative self-labeling process, mistakes can be propagated ifno amending procedure is used. In this paper, we build upon aninterpretable self-labeling grey-box classifier that uses a blackbox to estimate the missing class labels and a white box to makethe final predictions. We propose a Rough Set based approach foramending the self-labeling process. We compare its performanceto the vanilla version of our self-labeling grey-box and theuse of a confidence-based amending. In addition, we introducesome measures to quantify the interpretability of our model.The experimental results suggest that the proposed amendingimproves 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 Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 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 Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (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 unlabeleddata during the learning phase in order to increaseclassifier{\textquoteright}s generalization capability. However, most successfulsemi-supervised classifiers involve complex ensemble structuresand iterative algorithms which make it difficult to explain theoutcome, thus behaving like black boxes. Furthermore, duringan iterative self-labeling process, mistakes can be propagated ifno amending procedure is used. In this paper, we build upon aninterpretable self-labeling grey-box classifier that uses a blackbox to estimate the missing class labels and a white box to makethe final predictions. We propose a Rough Set based approach foramending the self-labeling process. We compare its performanceto the vanilla version of our self-labeling grey-box and theuse of a confidence-based amending. In addition, we introducesome measures to quantify the interpretability of our model.The experimental results suggest that the proposed amendingimproves 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 = "Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)",
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/",
}