## 1 Introduction  
Class imbalance is a prevalent challenge in machine learning (ML), particularly in classification tasks where the distribution of data classes is significantly skewed. This occurs when one class, referred to as the majority class, vastly outnumbers another, known as the minority class [1, 2]. Such imbalances can lead to models that perform well on the majority class but poorly on the minority class. For example, in a dataset where 95% of samples belong to one class and only 5% to another, a model could achieve 95% accuracy by always predicting the majority class, while completely failing to identify instances of the minority class. Studies have shown that ML methods trained on imbalanced datasets tend to favor the majority class, resulting in poor predictive accuracy for the minority class, which is often the class of the greatest interest [3; 4]. This issue is particularly critical in domains such as healthcare, where rare conditions or events are of high importance. For instance, in asthma management, severe class imbalance can hinder the performance of models predicting asthma attacks [5; 6]. Similarly, in remote sensing imagery classification, datasets often exhibit significant imbalance, necessitating the careful consideration of evaluation metrics and classification techniques [7; 8]. To address class imbalance, researchers employ data-level and algorithm-level techniques. Data-level methods include oversampling techniques like the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic samples for the minority class, and undersampling methods that reduce the number of majority class samples [5; 7]. Algorithm-level methods involve modifying the learning algorithm itself, such as cost-sensitive learning which assigns higher penalties for misclassifying minority class samples [7; 8]. SMOTE, in particular, generates synthetic examples of the minority class rather than replicating existing instances [4; 6]. It selects a minority class instance and creates synthetic examples along the line segments connecting that instance to its nearest minority class neighbors [9; 10]. This approach expands the minority class' feature space, enabling classifiers to identify decision regions more favorable to minority instances. Unlike simple oversampling methods, which risk overfitting by duplicating data, SMOTE generates diverse, unique samples, helping models generalize better to unseen data [10]. SMOTE has demonstrated its effectiveness across various domains, including fraud detection, medical diagnosis, and risk management. For example, in medical datasets where certain diseases are rare, SMOTE can improve detection rates, ultimately contributing to better patient outcomes [11]. Additionally, SMOTE is algorithm-independent, making it a versatile preprocessing step for any classification algorithm. Its simplicity and effectiveness have led to the development of extensions like Borderline-SMOTE, which focuses on generating synthetic samples near decision boundaries to improve classification in critical regions [12]. Despite its advantages, SMOTE has limitations. The introduction of synthetic data can sometimes lead to overlapping between classes, potentially reducing model performance. To address this, hybrid approaches like SMOTE combined with Edited Nearest Neighbors (SMOTEENN) have been proposed [11]. SMOTEENN integrates SMOTE with the Edited Nearest Neighbors (ENN) algorithm to enhance classifier performance on imbalanced data [11]. SMOTE generates synthetic minority class examples by interpolating between existing minority instances and their nearest neighbors, increasing the minority class population without duplicating samples. ENN then refines the dataset by removing noisy and ambiguous instances from both the majority and minority classes, particularly those misclassified by their \(k\)-nearest neighbors.