## 2 Related Work  
The classification from imbalanced datasets has been the target of intensive research for many years. As mentioned in the introduction, there are different ways of dealing with this drawback, although the most extended involve data preprocessing by means of resampling strategies. The synthetic minority oversampling technique (SMOTE) is a widely used oversampling method for creating artificial instances of the minority class by introducing new computed examples along the line segments joining the k minority class nearest neighbors. Several extensions of SMOTE have been developed with the aim of improving the precision of the minority class under different circumstances. Some of them involve the creation of instances performed by SMOTE to specific parts of the input space within the limits of the positive class [13; 14], while others use noise filters after the application of SMOTE. [PERSON] et al. [15] proposed the SMOTE-IPF resampling method, which extends SMOTE with an ensemble-based noise filter called the iterative-partitioning filter (IPF); the said filter can overcome the problems produced by noisy and borderline examples in imbalanced datasets. Recently a neural network-based oversampling approach has been used in some works in the literature to generate minority class instances. In these works, unlike SMOTE-based methods that do not take into account the data distribution, generative adversarial networks (GAN) algorithms are used for learning class-dependent data distribution and produce the generative model [16]. These techniques are mainly applied in the image processing area, where several GAN variants have been implemented in order to generate synthetic images [17]. Although the results are promising, the main drawback of this approach in comparison with classic oversampling strategies stems from its higher complexity and computational time, since a time-consuming training process is required to induce the models. Moreover, GAN methods are not free from the overfitting problem. Another popular sampling approach is random under-sampling (RUS). This technique balances the examples of the dataset by randomly removing some of them from the majority class. Its main drawback is the elimination of data that could be important for inducing the classification model [9]. Condensed nearest neighbor (CNN) [18] is a data-reduction strategy that was not initially designed for dealing with imbalanced classification but for improving the efficiency of the nearest neighbor classification algorithm. CNN identifies prototype points from the original dataset to be used to classify new instances. It uses an iterative procedure, which starts with a subset containing a prototype point randomly selected. Then, the remaining points in the dataset are classified by the NN rule using the points in the prototype subset. Those that are classified incorrectly are added to the subsets. A similar proposal put forward by [PERSON] [19] adds a pre-processing step involving the removal of noise and borderline examples in order to obtain further data reduction without increasing the classification error rate. Edited nearest neighbor (ENN) [20] is a similar strategy that includes a preliminary pass to remove points of the majority class. These are the examples whose neighbors belong mostly to the other class. NearMiss [21] is a family of methods where the majority class is undersampled by means of a procedure meant to remove examples based on their distance to other examples of the same class. NearMiss-1 retains the majority class samples which are close to some minority class samples, since their average distances to the nearest minority class samples are the smallest. NearMiss-2 selects the majority class samples for which the average distances to the kfarthest minority class samples are the smallest. For each minority sample, NearMiss3 selects the k closest majority class samples.