Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance
- Shiven Sharma
- Colin Bellinger
- Bartosz Krawczyk
- Nathalie Japkowicz
- Osmar R. Zaiane, University of Alberta (Database)
The class imbalance problem is a pervasive issue in many real-world domains. Oversampling methods that inflate the rare class by generating synthetic data are amongst the most popular techniques for resolving class imbalance. However, they concentrate on the characteristics of the minority class and use them to guide the oversampling process. By completely overlooking the majority class, they lose a global view on the classification problem and, while alleviating the class imbalance, may negatively impact learnability by generating borderline or overlapping instances. This becomes even more critical when facing extreme class imbalance, where the minority class is strongly underrepresented and on its own does not contain enough information to conduct the oversampling process. We propose a novel method for synthetic oversampling that uses the rich information inherent in the majority class to synthesize minority class data. This is done by generating synthetic data that is at the same Mahalanbois distance from the majority class as the known minority instances. We evaluate over 26 benchmark datasets, and show that our method offers a distinct performance improvement over the existing state-of-the-art in oversampling techniques.
Citation
S. Sharma, C. Bellinger, B. Krawczyk, N. Japkowicz, O. Zaiane. "Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance". IEEE International Conference on Data Mining (ICDM), Singapore, November 2018.| Keywords: | imbalance, synthetic oversampling, classification |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Sharma+al:ICDM18,
author = {Shiven Sharma and Colin Bellinger and Bartosz Krawczyk and Nathalie
Japkowicz and Osmar R. Zaiane},
title = {Synthetic oversampling with the majority class: A new perspective on
handling extreme imbalance},
booktitle = {IEEE International Conference on Data Mining (ICDM)},
year = 2018,
}Last Updated: November 03, 2019Submitted by Sabina P
