###### Abstract
This type of bias, where the classes are not equally distributed, is well-known as the class imbalance problem, which causes trained algorithms to favor the predominant class in their predictions [14; 15]. This issue becomes severe when minority classes are of major interest, which means the cost of incorrectly labeling an example of an underrepresented class is very high [16]. Therefore, the data preprocessing step is paramount for transforming raw Big Data through data cleaning, extraction, and transformation, resulting in reduced and cleaned data, also known as smart data [7; 9; 17]. Solutions to the class imbalance problem can be categorized into two large groups called algorithm-level and data-level approaches. Algorithm-level methods modify existing algorithms to learn from imbalanced datasets. In contrast, data-level solutions focus on balancing the training dataset by either reducing (under-sampling) or increasing (oversampling) the sample sizes of different classes. Although data imbalance has traditionally been seen as the main cause of the poor performance of the classifiers, several studies have shown that other data irregularities, such as class overlap [18] and high dimensionality [19], can also exacerbate the problem, introducing a unique set of challenges. Consequently, a third research line has emerged, combining the strengths of both data-level and algorithm-level techniques. The class imbalance problem has attracted much attention for many years, leading to the development of numerous solutions addressing biased learning. Data-level solutions have been the most exploited of the three research lines because the proposals can be applied to various problems across different domains. For example, in 2002, [PERSON] et al. [20] introduced an oversampling technique called the Synthetic Minority Oversampling Technique (SMOTE) to balance datasets by generating synthetic examples of the minority class through interpolation between selected minority instances and its nearest neighbors. By 2019, [PERSON] [21] had documented approximately 85 variants of SMOTE. In the case of under-sampling techniques for selecting instances of the majority class to be removed, the community has adopted the strategy to filter those examples that can be considered noisy, redundant, or borderline. The simplest method to identify these data types is to analyze the local distribution of the data by computing the k-nearest neighbors [22]. Although the class imbalance problem has been effectively addressed in standard scenarios, Big Data introduces additional challenges where some conventional assumptions no longer hold, making traditional data preprocessing methods infeasible [7]. Therefore, data-level solutions must consider the specialized infrastructure to process large datasets in parallel or distributed systems such as Apache Spark and new programming paradigms like Scala [23]. However, it is possible to find strategies in the literature that have been adapted to be scalable solutions. For example, [PERSON] et al. [23] proposed SMOTE-Big Data (SMOTE-BD), an implementation in Apache Spark. This oversampling technique is based on an innovative distributed k-nearest neighbor model named kNN-IS that enhances the runtime efficiency of the nearest neighbor search [24]. In Big Data environments, similar to standard problems, data-level solutions are based on the nearest neighbor rule, typically employing metrics such as the Euclidean distance to assess the similarity between one example and another. However, computing distances between data samples in high-dimensional datasets can make them appear nearly identical, thereby complicating the differentiation between samples [25; 26]. A dataset with a large number of features relative to the number of samples can lead to the phenomenon known as the curse of dimensionality. In high-dimensional spaces, the volume of the space increases exponentially with the number of dimensions, leading to data sparsity.