Research Output
A Comparative Study of Assessment Metrics for Imbalanced Learning
  There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews several metrics for imbalanced learning in binary and multi-class problems. We emphasize considering class separability, imbalance ratio, and noise when choosing suitable metrics. Applications, advantages, and disadvantages of each metric are discussed, providing insights for different scenarios. By offering a comprehensive overview, this paper aids researchers in selecting appropriate evaluation metrics for real-world applications.

  • Date:

    31 August 2023

  • Publication Status:


  • Publisher

    Springer Nature Switzerland

  • DOI:


  • Funders:

    Historic Funder (pre-Worktribe)


Farou, Z., Aharrat, M., & Horváth, T. (2023). A Comparative Study of Assessment Metrics for Imbalanced Learning. In New Trends in Database and Information Systems: ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings (119-129).



Imbalanced learning, Assessment metrics, Classification

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