Methods for Managing Class Imbalance in Machine Learning Tasks

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Abstract
Class imbalance is a prevalent challenge in machine learning, particularly in domains like credit risk assessment, fraud detection, and medical diagnosis, where the minority class often represents the critical cases of interest. Traditional machine learning models tend to be biased towards the majority class, leading to suboptimal performance in detecting minority class instances. This paper explores various strategies to mitigate the effects of class imbalance, including performance metric adjustments, resampling techniques such as oversampling, undersampling, and Synthetic Minority oversampling Technique (SMOTE), as well as algorithmic modifications. We propose different hybrid approaches that combines Random Forest with Laplace regularization and others to enhance the classification of minority classes in imbalanced datasets. Our approach is validated using a credit risk dataset, where the proposed method demonstrates significant improvements in recall and F1 scores compared to baseline models, suggesting its effectiveness in addressing class imbalance in critical applications.

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