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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.
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 language | English |
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Title of host publication | Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
Publisher | IEEE |
Pages | 1-8 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-7281-6932-3 |
ISBN (Print) | 978-1-7281-6933-0 |
DOIs | |
Publication status | Published - 2020 |
Event | IEEE World Congress on Computational Intelligence (WCCI) 2020: IEEE International Conference on Fuzzy Systems - Glasgow, United Kingdom Duration: 19 Jul 2020 → … https://wcci2020.org/ |
Conference
Conference | IEEE World Congress on Computational Intelligence (WCCI) 2020 |
---|---|
Abbreviated title | FUZZ-IEEE |
Country | United Kingdom |
City | Glasgow |
Period | 19/07/20 → … |
Internet address |
Fingerprint
Dive into the research topics of 'An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling'. Together they form a unique fingerprint.Projects
- 1 Active
-
IRP8_a: IMAGica: an Integrative personalised Medical Approach for Genetic diseases, Inherited Cardiac Arrhythmias as a model
Nowe, A., Bonduelle, M., Brugada, P., Deschepper, R., Lenaerts, T., Van Dooren, S., De Asmundis, C., Gidron, Y., Bilsen, J. & De Couck, M.
1/07/16 → 30/06/23
Project: Fundamental
Activities
- 1 Talk or presentation at a conference
-
An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling
Isel Del Carmen Grau Garcia (Speaker)
20 Jul 2020Activity: Talk or presentation › Talk or presentation at a conference