A class imbalance problem occurs when a dataset is decomposed into one majority class and one minority class. This problem is critical in the machine learning domains because it induces bias in training machine learning models. One popular method to solve this problem is using a sampling technique to balance the class distribution by either under-sampling the majority class or over-sampling the minority class. So far, diverse over-sampling techniques have suffered from overfitting and noisy data generation problems. In this paper, we propose an over-sampling scheme based on the borderline class and conditional generative adversarial network (CGAN). More specifically, we define a borderline class based on the minority class data near the majority class. Then, we generate data for the borderline class using the CGAN for data balancing. To demonstrate the performance of the proposed scheme, we conducted various experiments on diverse imbalanced datasets. We report some of the results.
Bibliographical noteFunding Information:
This research was supported in part by Energy Cloud R&D Program (Grant Number: 2019M3F2A1073184) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT and in part by Government-wide R&D Fund project for infectious disease research (GFID), Republic of Korea (Grant Number: HG19C0682).
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
- Borderline minority class
- Conditional generative adversarial network (CGAN)
- Imbalanced data
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture