Enhanced Wasserstein Generative Adversarial Network (EWGAN) to Oversample Imbalanced Datasets

Muhammad Hassan Ajmal Hashmi, Muhammad Ashraf, Saleem Zubair Ahmad, Muhammad Waseem Iqbal, Adeel Hamid, Dr. Abid Ali Hashmi and Muhammad Ameer Hamza
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Muhammad Hassan Ajmal Hashmi: CEO Tech Solutions Lahore-54000 Pakistan, Faculty of Computer Science, KIPS College Lahore-5400 Pakistan
Muhammad Ashraf: IT Department ,Gulab Devi Teaching Hospital Lahore 54000, Pakistan
Saleem Zubair Ahmad: Department of Software Engineering, Superior University Lahore-54000, Pakistan
Muhammad Waseem Iqbal: Department of Software Engineering, Superior University Lahore-54000, Pakistan
Adeel Hamid: Faculty of Computer Science, Virtual University Lahore-5400, Pakistan
Dr. Abid Ali Hashmi: Educational Complex Lahore-54000 Pakistan
Muhammad Ameer Hamza: Department of Computer Science, Superior University Lahore-54000, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 3, 385-395

Abstract: This paper examines WGAN as a more advanced technique for addressing imbalanced data sets in the context of machine learning. A variety of domains, including medical diagnosis and image generation, are affected by the problem of imbalanced datasets since it is essential to represent the minority class to train a satisfactory model and create various types of data. To overcome these challenges WGAN uses some features such as; Residual connections in the critic network, better sampling for minority classes, and some noise and sample reshaping. These innovations contribute to the increased stability of the model, the quality of synthetic data, and the distribution of classes in a dataset. The comparative analysis of WGAN with basic GAN and Improved GAN has shown the effectiveness of the given algorithm in terms of producing high-quality diversified synthetic data that is closer to the real data distribution. The study identifies the future research direction of WGAN in enhancing machine learning based on reliable and diverse synthesized data, providing new insights and directions for future studies and practical applications in tackling data imbalance issues.

Keywords: WGAN; Imbalanced Data; Synthetic Data; Machine Learning; Cancer Diagnosis; Data Sampling; Model Stability; Data Generation; GAN Models (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:3:p:385-395

DOI: 10.61506/01.00505

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