## 2 Related Work
On the other hand, validation is carried out by means of three classic metrics (F-measure, G-mean and AUC), which sometimes give contradictory results. No advanced metrics for imbalanced data classification have been applied. Other work where the behavior of base and ensemble classifiers is analyzed in imbalanced data contexts was carried out in [14]. The authors performed a review of the methods proposed for dealing with the problem of imbalanced data classification and provided a taxonomy to classify them. The goal of the work is to analyze the preprocessing methods, including resampling strategies, and compare their efficacy as a step prior to the classification. The cost of the incorrect classifications for each class is also considered, which requires involving human experts. Some data intrinsic characteristics are analyzed in the study. The authors conclude that issues such as the lack of density, small sample size and class overlapping have a greater influence in the performance of classifiers than the imbalance ratio. However, these conclusions cannot be generalized since the datasets used in the study have a low imbalance ratio and most of them have a low number of attributes and examples. In [4], a clustering-based undersampling strategy combined with ensemble classifiers is proposed and tested with only one dataset in a medical application domain. An ensemble approach is also presented in [43] for assisted reproductive technology outcome prediction. The ensemble is built with two incremental classifiers that can update themselves with new data. Both studies focus on solving the imbalance problem in very specific cases. The impact of class imbalance ratio for a particular kind of classifiers has also been analyzed in other studies whose main purpose is helping to select the best resampling method regarding the values of that factor [44]. Other works focused on specific classification algorithms are devoted to proposing sampling approaches to improve their performance. In [45], a hybrid procedure combining both oversampling and undersampling with a support vector machine (SVM) ensemble manages to improve the reliability of that classifier. A random forests quantile classifier is proposed in [46] for class imbalanced data. Other study addresses imbalanced data classification in the area of cancer diagnosis by means of a feature selection method and a cost sensitive support vector machine (CSSVM) learning algorithm [2]. The study of the performance of neural networks and deep learning algorithms in unbalanced data contexts is much scarcer, being mostly focused in the areas of image and text processing where the volume of data is very high. Generative adversarial networks (GAN) have been used as basis of oversampling strategies in [47]. There are proposals in the literature for using them to generate sentences in text processing [10] and variants of GANT to generate synthetic images [48] in order to balance data. Complexity and computational cost are the main drawbacks of this approach. In addition, text generation models for imbalanced datasets based on deep learning algorithms, such as LSTM (long short-term memory) networks, are also found in the literature [49]. The performance of deep learning algorithms on imbalanced datasets is similar to that of traditional algorithms, except when working with large datasets. For this reason, these techniques are also often used in combination with resampling strategies in the presence of uneven distributions [50; 51]. In [50], several classifiers were tested on a highly imbalanced dataset composed by 146,500 instances, both in its original form and resampled by several undersampling and oversampling strategies. The results showed that random forest (RF) outperformed deep learning algorithms. Two imbalanced datasets of similar size to the above reference were used in [51] to test a resampling technique proposed in the work with different machine learning algorithms including RF and LSTM.