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Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets
Arslan, Yusuf; Allix, Kevin; Lefebvre, Clément et al.
2022In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Peer reviewed
 

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Keywords :
Prototypical Explanations; Undersampling; Imbalanced Datasets
Abstract :
[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.
Disciplines :
Computer science
Author, co-author :
Arslan, Yusuf ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Allix, Kevin ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Lefebvre, Clément
Boytsov, Andrey
Bissyande, Tegawendé François D Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Klein, Jacques ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
External co-authors :
no
Language :
English
Title :
Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets
Publication date :
2022
Event name :
21st IEEE International Conference on Machine Learning and Applications
Event date :
from 12-12-2022 to 14-12-2022
Audience :
International
Main work title :
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Pages :
1449-1454
Peer reviewed :
Peer reviewed
FnR Project :
FNR13778825 - Explainable Machine Learning In Fintech, 2019 (01/07/2019-30/06/2022) - Jacques Klein
Funders :
FNR - Fonds National de la Recherche [LU]
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