## 2 Related Work
The proposal was compared with popular resampling techniques such as random undersampling (RUS), random oversampling (ROS) or SMOTE. In most experiments RF provided the highest values for all performance metrics. The results of LSTM were unequal since only with RUS and for one of the datasets LSTM achieved the best values for all metrics. From the analysis of these and other works we can deduce that random forest combined with different resampling strategies can be a good choice to deal with classification of unbalanced data. Only in domains with high data volume, such as image or text processing, may the use of deep learning techniques be more appropriate. The focus of some proposals in the literature is improving the classification performance in specific application domains. In [52], SVM is used jointly with SMOTE oversampling technique for preprocessing imbalanced data related to caravan car policy. This study analyzes the values of sensitivity obtained after applying several machine learning algorithms to preprocessed data. Given that the work is focused on a unique dataset, factors affecting performance of the prediction are not studied. Moreover, no recent specific metrics for imbalanced data classification are used in the validation. In the field of sentiment classification, there are a number of similar proposals, such as the modification of a SVM-based ensemble algorithm by incorporating both oversampling and undersampling [53]. In the area of vaccine design, the method CluSMOTE [54], which combines a cluster-based undersampling approach and a synthetic minority oversampling technique, is proposed to improve the results of AUC and G-mean provided by the SVM algorithm. Web application security is a field where the imbalanced data problem is also present. [PERSON] et al. [55] tackled it by means of a novel approach for generating synthetic samples of minority class that have identical distribution as real scenarios by using generative adversarial networks. All these proposals are addressed to specific classification methods or domains, making them difficult to apply across the board. Other research interest is to improve existing resampling methods. In [56], the SMOTE strategy is extended by adding an iterative ensemble-based noise filter called iterative partitioning filter (IPF) in order to deal with problems in the classification performance produced by noisy and borderline examples in imbalanced datasets. The results of this extension, named SMOTE-IPF, are better than those yielded by the basic SMOTE and other SMOTE-based methods. The validation is carried out with different real and synthetic datasets in a not too large range of number of instances, number of attributes and imbalance ratio. [PERSON] and coworkers [57] proposed a new resampling method by combining oversampling and undersampling techniques. In this study the authors try to minimize both the overfitting problem that happens when using random oversampling and the information lost associated to the random undersampling technique. Their results cannot be generalized since the method has not been tested with highly imbalanced datasets. In [58], SMOTE is combined with undersampling strategies such as edited nearest neighbor or Tomek link, explained in the nest sections, to address the multiclass imbalance problem. [PERSON] et al. [59] present a proposal for improvement of SMOTE and its variants based on oversampling and undersampling. SMOTE is used in the first step for oversampling. Then, the undersampling process is performed by new extensions of KNN classifiers based on fuzzy sets. Recently, deep learning-based oversampling methods such as variational autoencoders have been developed [60; 61] although they are mainly indicated for image processing. The ultimate goal of this study is to find patterns that relate the characteristics of the data to the most appropriate resampling strategy according to those characteristics. Association rules are a valuable tool for finding patterns, especially in contexts of data sparsity.