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Authors: Daniel Lehmann and Marc Ebner

Affiliation: Institut für Mathematik und Informatik, Universität Greifswald Walther-Rathenau-Straße 47, 17489 Greifswald, Germany

Keyword(s): Undersampling, Clustering, CNN.

Abstract: Image classification problems are often class-imbalanced in practice. Such a class imbalance can negatively affect the classification performance of CNN models. A State-of-the-Art (SOTA) approach to address this issue is to randomly undersample the majority class. However, random undersampling can result in an information loss because the randomly selected samples may not come from all distinct groups of samples of the class (subclasses). In this paper, we examine an alternative undersampling approach. Our method undersamples a class by selecting samples from all subclasses of the class. To identify the subclasses, we investigated if clustering of the high-level features of CNN models is a suitable approach. We conducted experiments on 2 real-world datasets. Their results show that our approach can outperform a) models trained on the imbalanced dataset and b) models trained using several SOTA methods addressing the class imbalance.

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Paper citation in several formats:
Lehmann, D. and Ebner, M. (2022). Subclass-based Undersampling for Class-imbalanced Image Classification. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 493-500. DOI: 10.5220/0010841100003124

@conference{visapp22,
author={Daniel Lehmann. and Marc Ebner.},
title={Subclass-based Undersampling for Class-imbalanced Image Classification},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={493-500},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010841100003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Subclass-based Undersampling for Class-imbalanced Image Classification
SN - 978-989-758-555-5
IS - 2184-4321
AU - Lehmann, D.
AU - Ebner, M.
PY - 2022
SP - 493
EP - 500
DO - 10.5220/0010841100003124
PB - SciTePress