Dealing with Class Imbalance in Machine Learning: Performance Metrics and Data Balancing Methods

This work addressed the class imbalance problem in machine learning and identified performance metrics suited to unbalanced datasets. Extensive experiments were conducted on both simulated and real-life data using various machine learning algorithms and data balancing techniques. The findings highlighted the limitations of accuracy and emphasized that while ROC-AUC was a more suitable metric for evaluating model performance, balancing the dataset was essential for obtaining reliable results. Furthermore, the stability of the Matthews Correlation Coefficient (MCC) before and after data balancing underscored its robustness as a performance measure. Additionally, the study revealed that while various data-balancing techniques had a similar impact on improving machine learning model performance, undersampling consistently outperformed other methods by enhancing the performance of the Random Forest model in terms of the ROC-AUC metric across all cases. Supplementary Material File (ml_research_paper.pdf) - Download - 2.23 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 183views 1376downloads Citations Download citation Yesar Ahmed Oshan, Oleg Makhnin, Anwar Hossain. Dealing with Class Imbalance in Machine Learning: Performance Metrics and Data Balancing Methods. Authorea. 31 August 2025. DOI: https://doi.org/10.22541/au.175666516.63936610/v1 DOI: https://doi.org/10.22541/au.175666516.63936610/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.