BCGAN: A CGAN-based over-sampling model using the boundary class for data balancing

Minjae Son, Seungwon Jung, Seungmin Jung, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalJournal of Supercomputing
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Borderline minority class
  • Conditional generative adversarial network (CGAN)
  • Imbalanced data
  • Over-sampling

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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