Integrated Approaches for Handling Imbalanced Data: Techniques, Applications and Future Directions
Class imbalance poses a significant challenge in machine learning, particularly in critical fields such as healthcare, fraud detection, and natural language processing. The underrepresentation of minority classes often results in models that fail to detect rare but crucial events, such as identifying rare diseases or fraudulent transactions. This paper aims to review advancements in integrated techniques for addressing class imbalance, focusing on their applications and challenges. A comprehensive review of data-level methods, including SMOTE (Synthetic Minority Oversampling Technique), algorithm-level adjustments like cost-sensitive learning, and hybrid techniques such as SMOTEBoost and RUSBoost was conducted. These approaches were analyzed for their effectiveness in improving model performance on minority class detection. Findings indicate that integrated techniques enhance the detection of minority instances while maintaining overall model accuracy. However, computational demands and fairness issues remain key challenges, necessitating further research into interpretable and efficient solutions.
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