A Comparative Study of Assessment Metrics for Imbalanced Learning

Farou, Zakarya; Aharrat, Mohamed; Horváth, Tomáš

Abstract

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.

Citation

Farou, Z., Aharrat, M., & Horváth, T. (2023, September). A Comparative Study of Assessment Metrics for Imbalanced Learning. Presented at European Conference on Advances in Databases and Information Systems (ADBIS 2023), Barcelona, Spain

• Presentation Conference Type: Conference Paper (published)
• Conference Name: European Conference on Advances in Databases and Information Systems (ADBIS 2023)
• Start Date: Sep 4, 2023
• End Date: Sep 7, 2023
• Online Publication Date: Aug 31, 2023
• Publication Date: 2023
• Deposit Date: Apr 8, 2024
• Publisher: Springer
• Pages: 119-129
• Series Title: Communications in Computer and Information Science (CCIS)
• Series Number: 1850
• Series ISSN: 1865-0929
• Book Title: 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
• ISBN: 9783031429408
• DOI: https://doi.org/10.1007/978-3-031-42941-5_11
• Keywords: Imbalanced learning, Assessment metrics, Classification
• Public URL: http://researchrepository.napier.ac.uk/Output/3587409
• Related Public URLs: http://adbis.eu/