Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

Shahnawaz Ayoub, Yonis Gulzar, Jaloliddin Rustamov, Abdoh Jabbari, Faheem Ahmad Reegu, Sherzod Turaev

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.

Original languageEnglish
Article number7097
JournalSustainability (Switzerland)
Issue number9
Publication statusPublished - May 2023


  • computer vision
  • deep learning
  • imbalanced dataset
  • machine learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


Dive into the research topics of 'Adversarial Approaches to Tackle Imbalanced Data in Machine Learning'. Together they form a unique fingerprint.

Cite this