Evaluating the impact of point-biserial correlation-based feature selection on machine learning classifiers: a credit card fraud detection case study

Authors

  • Ahmed A.H. Alkurdi Department of Information Technology, Duhok Technical College, Duhok Polytechnic University, Duhok, KRG-Iraq; Department of Computer Science, College of Science, Nawroz University, Duhok, KRG-Iraq.
  • Renas R. Asaad Department of Computer Science, College of Science, Nawroz University, Duhok, KRG-Iraq. Department of Technical Informatics, Technical College of Informatics, Akre University for Applied Science, Duhok, KRG-Iraq.
  • Saman M Almufti Department of Computer Science, College of Science, Nawroz University, Duhok, KRG-Iraq Department of Technical Informatics, Technical College of Informatics, Akre University for Applied Science, Duhok, KRG-Iraq
  • Nawzat S. Ahmed Department of Information Technology, Duhok Technical College, Duhok Polytechnic University, Duhok, KRG-Iraq

Keywords:

Credit Card, Fraud, Machine learning, Predictive performance, PBC-based feature selection

Abstract

Objective: This article examines the factors influencing the awareness and adoption of International Public Sector Accounting Standards (IPSAS) in public units in Vietnam. It seeks to identify key challenges and drivers that affect the understanding and implementation of these standards.

Methods: The study uses a survey methodology, gathering responses from a sample of public service units in Vietnam. The survey is designed to assess the level of awareness and readiness of these units to adopt IPSAS, considering variables such as management support, training, and technical infrastructure. Statistical analysis was performed to determine the most influential factors.

Results: The findings highlight that managerial support, adequate training, and access to proper technical infrastructure are crucial for successful IPSAS implementation. Lack of awareness, insufficient training, and resource limitations are the primary barriers to the adoption of these standards. Public units that have higher levels of awareness and better access to resources are more likely to successfully implement IPSAS.

Contribution: The study provides valuable insights into the process of adopting IPSAS in Vietnam’s public sector. It offers recommendations for improving training programs, enhancing managerial support, and strengthening the technical capacity of public units to ensure smoother implementation of the standards.

Conclusion: The implementation of IPSAS in Vietnam's public sector is affected by several key factors, including awareness, training, and infrastructure. Strengthening these areas can significantly improve the adoption process and enhance transparency and accountability in public financial management.

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2024-03-25

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A.H. Alkurdi, A., R. Asaad, R., M Almufti, S., & S. Ahmed, N. (2024). Evaluating the impact of point-biserial correlation-based feature selection on machine learning classifiers: a credit card fraud detection case study. Journal of Management & Technology, 24, 166–196. Retrieved from https://revistagt.fpl.emnuvens.com.br/get/article/view/2882

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Vol. 24 (2024)

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