## 4 Discussion  
Recent advances in addressing class imbalance for regression tasks have built upon techniques originally devised for imbalanced classification. One common approach is to generate synthetic data for underrepresented classes using variants of SMOTE. Although SMOTE was initially proposed for classification, researchers have adapted it to regression settings by interpolating both feature inputs and the target variable, aiming to produce realistic data points that improve the model's ability to learn from scarce examples [22]. Extensions of SMOTE for regression, such as SMOGN (SMOTE for Regression with Gaussian Noise), incorporate noise within the interpolation step to better capture the variability inherent to continuous target variables [23]. These strategies generally align with findings that data-level manipulations can substantially increase performance when class or target distributions are heavily skewed. Beyond data-level methods, cost-sensitive learning has gained traction as another potent strategy for tackling class imbalance in regression. In cost-sensitive approaches, the learning algorithm's loss function is modified to penalize errors on the minority (or difficult-to-predict) segments of the regression span more heavily [24, 25]. This technique effectively shifts the model's emphasis toward minimizing high-cost errors, thus promoting more balanced predictions across uneven target distributions. By customizing the cost function for imbalanced regression, models can focus on critical or rare target ranges without unduly sacrificing performance in better-represented regions [26]. Although cost-sensitive learning is widely applied in classification scenarios, multiple studies have underscored its adaptability and success in imbalanced regression problems as well [25, 27]. A growing body of work also explores ensemble-based techniques, such as boosting and bagging, specifically tailored for imbalanced regression [28]. These methods combine multiple weak learners and adaptively focus on regions of the feature space where performance is poorer. For instance, recent studies have integrated oversampling or undersampling processes within a boosting architecture, allowing each iteration to learn more effectively from challenging subsets of the data [28]. Similarly, hybrid methods merge data augmentation strategies (like SMOTE variants) with cost-sensitive ensembles, yielding promising results in predictive power and model stability [22, 28]. Overall, the continued refinement of synthetic oversampling, cost-sensitive optimization, and ensemble methods underscores the field's commitment to developing robust solutions for imbalanced regression tasks across diverse real-world applications. SMOTEENN seamlessly combines SMOTE's synthetic data generation with Edited Nearest Neighbors (ENN) to remove noisy and borderline instances from the enriched dataset, leading to more refined and balanced training samples [17]. This dual-stage process not only addresses the core issue of minority class underrepresentation but also reduces confusion between classes by pruning ambiguous data points, an essential step often omitted in other SMOTE variants [22]. As a result, SMOTEENN has been shown to produce cleaner decision boundaries and more robust performance metrics, including higher accuracy and lower error rates in both classification and regression tasks [22, 28]. Studies indicate that this enhanced data purity and increased representativeness allow learning algorithms to generalize more effectively, ultimately making SMOTEENN a superior resampling choice for heavily imbalanced datasets compared to stand-alone SMOTE or other SMOTE-based techniques. The SMOTE learning curves consistently exhibit unhealthy characteristics across all sampling strategies due to the absence of a small gap between training and validation performance, despite the validation score eventually converging to the training score. This convergence suggests that the model may be overfitting and struggling to generalize effectively to unseen data.