Reference : Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/54386
Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets
English
Arslan, Yusuf mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Allix, Kevin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Lefebvre, Clément mailto []
Boytsov, Andrey mailto []
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
2022
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
1449-1454
Yes
No
International
21st IEEE International Conference on Machine Learning and Applications
from 12-12-2022 to 14-12-2022
[en] Prototypical Explanations ; Undersampling ; Imbalanced Datasets
[en] Among the reported solutions to the class imbalance issue, the undersampling approaches, which remove instances of insignificant samples from the majority class, are quite prevalent. However, the undersampling approaches may discard significant patterns in the datasets. A prototype, which is always an actual sample from the data, represents a group of samples in the dataset. Our hypothesis is that prototypes can fill the missing significant patterns that are discarded by undersampling methods and help to improve model performance. To confirm our intuition, we articulate prototypes to undersampling methods in the machine learning pipeline. We show that there is a statistically significant difference between the AUPR and AUROC results of undersampling methods and our approach.
Luxembourg National Research Fund (FNR)
Researchers
http://hdl.handle.net/10993/54386
10.1109/ICMLA55696.2022.00228
FnR ; FNR13778825 > Jacques Klein > ExLiFT > Explainable Machine Learning In Fintech > 01/07/2019 > 30/06/2022 > 2019

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