## 2 Background
According to some authors [32], these circumstances often cause learning models to fail in detecting minority patterns because:  
1. those observations usually overlap with the majority region,
2. data analysis techniques may confuse minority examples with noise or outlier data (and vice versa),
3. good coverage of the majority examples distorts the minority examples, and
4. a small sample with a lack of density but with high feature dimensionality makes it difficult to identify a pattern. Sampling approaches are extensively studied ways to mitigate the consequences of class imbalance [33]. They contribute to alleviating the effect of the skewed class distribution by balancing the sample space for an imbalanced dataset. Sampling techniques are essentially preprocessing methods, yet highly versatile because of their independence of the selected classifier. The two basic strategies of resampling are:  
* Oversampling approaches: They synthetically generate new minority class examples. Two popular methods are
* Random oversampling (ROS). This approach generates random examples following the distributional properties of the minority class to make this space denser. * Synthetic minority over-sampling technique (SMOTE). This approach generates new synthetic examples along the line between the minority examples and their selected nearest neighbors [34]. * Borderline SMOTE. This algorithm is a variant of the original SMOTE algorithm. Here, only the minority examples near the borderline are oversampled [35]. * Adaptive synthetic oversampling (ADASYN): the idea behind ADASYN is to generate more synthetic entries for the minority class examples that are harder to learn. This algorithm considers a weighted distribution for different minority class examples by their level of difficulty in learning [36]. * Undersampling approaches: they discard the intrinsic samples in the majority class. The simplest yet most effective method is random undersampling (RUS), which involves the elimination of majority class examples at random. Regarding the capacity to classify, it is widely accepted that \(k\)-fold cross-validation is the basis for evaluating the performance of classifiers. Here, the dataset is split into \(k\) subsets, and the classifier then takes \(k-1\) folds to train and, subsequently, it uses the remaining fold to predict. This process is repeated \(k\) times using a different fold to evaluate the classifier in each iteration. Subsequently, several metrics can be calculated to assess the classifier. In imbalanced learning, the focus is on four of the so-called threshold measures: sensitivity, precision, specificity, and \(F\)-measure. Table 1 summarizes such metrics according to the literature [37; 38]. Besides, there are other measures (called ranking metrics) that can be calculated in function of the threshold metrics, and they are restricted to the binary classification task. Ranking metrics are a way of evaluating classifiers based on their effectiveness of separating classes. These metrics are:  
* ROC-AUC. The ROC--Receiver Operating Characteristic--is the curve formed when the transversal axis represents the 'false positive rate' (1-specificity), and the longitudinal axis represents the 'true positive rate' (sensitivity) for different cut-off points. ROC is a probability distribution, and its area under the curve (AUC) represents the degree of separability between classes. A ROC-AUC value close to 1 indicates that the classifier has excellent performance when separating classes, and a value close to 0.5 indicates that the classifier cannot discriminate correctly. * PR-AUC. Like the ROC curve, the PR (Precision-Recall) curve is a plot of the precision (\(y\)-axis) and the recall (\(x\)-axis) for different probability thresholds. PR curve is a useful diagnostic tool for imbalanced binary models because it emphasizes the performance of a classifier in terms of the minority class, this way, its area under the curve (PR-AUC) summarizes the distribution, where a value of 1.0 represents a model with perfect skill.