Exploiting Prototypical Explanations for 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.

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 :

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]

Available on ORBilu :

since 13 February 2023

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