arXiv:2506.01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability. This is particularly detrimental in applications aimed at predicting rare events that fall outside of the domain of the bulk of the training data. In this study, we review the current state-of-the-art regarding sampling-based methods and cost-sensitive learning. Additionally, we propose novel approaches to mitigate model bias. To better assess the importance of data, we introduce the density-dista
The increasing complexity and application of AI in real-world scenarios highlight the persistent challenge of imbalanced datasets, which degrade model reliability and fairness.
Addressing data imbalance is crucial for developing robust and trustworthy AI systems, particularly in critical applications where rare events have significant consequences.
This research provides methods to improve AI model stability and performance when dealing with skewed data distribution, enhancing the practical utility and ethical implications of AI.
- · AI developers
- · Industries relying on AI for critical predictions
- · Data scientists
- · AI models without robust data imbalance mitigation
- · Applications in underrepresented data domains
More reliable AI predictions across various applications, even with challenging datasets.
Increased trust and adoption of AI systems due to improved fairness and accuracy in edge cases.
Reduced risk of AI failures in high-stakes environments, potentially accelerating AI integration into sensitive sectors.
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