## 2 Related Work
Regarding external methods, data resampling strategies have received considerable attention in machine learning since they are one of the most reliable approaches to deal with the class imbalance problem. Resampling methods try to overcome imbalanced class distribution drawback by either adding or removing examples from the dataset. Among all the sampling strategies in the literature, SMOTE (Synthetic Minority Over-Sampling Technique) [35] is one of the most widely used. In this method, a nearest neighbor-based interpolation procedure is used to generate examples of the minority class. Tomek Link (T-Link) [36] is other popular technique that also considers nearest neighbor examples, but in this case for undersampling purposes. Other commonly used undersampling, oversampling and hybrid approaches are described in detail in the next section. They have been proposed aiming at improving the precision of the minority class in imbalanced data contexts. However, their behavior is not the same for all datasets. There are also hybrid approaches that combine specific algorithms with resampling techniques. A fuzzy rule-based classifier that is used with resampling techniques in [37]. In [38] a new data reduction procedure is presented to be used in combination with an SVM-based classification algorithm. The method, based on the fuzzy adaptive resonance theory, involves the modification of the data distribution. According to [39], classifier performance depends on some factors such as imbalance ratio between the majority and minority classes, dataset characteristics, classification algorithms and resampling methods. Classification model reliability can be significantly improved when imbalance ratio between the majority and the minority class is reduced by using suitable resampling methods. The effects of the mentioned factors on the performance have been evaluated in some works, however, either factors are mainly analyzed in an individual way without considering them jointly or a not very wide range of data property values are considered. Moreover, in most of the works, classic metrics are used for validation. In [40], the effect of basic resampling methods on the performance of C5.0 decision trees is discussed. Classifiers induced from a reduced number of real-world datasets are tested by obtaining the error rate on each class. A study of the effectiveness of several sampling techniques is presented in [41]. In this work, improvement of the performance of a logistic regression classifier in presence of data imbalance is examined aiming at finding out the best strategy to be used. Only the precision of the majority class and the recall of the minority class are obtained for evaluating the effect of the sampling strategies on the classification. Other drawback of this work is the use of a unique synthetic dataset in the study. Although there are some other studies about resampling techniques, their authors do not specify which resampling techniques can be used according to specific datasets characteristics and machine learning algorithms. The behavior of ensemble classifiers with imbalanced data has also been a subject under study. [PERSON] et al. [42] presented an extensive experimental study where various ensemble-based methods are tested in its original form and modified with several diversity enhancing techniques. All the algorithms are applied to 84 imbalanced datasets before and after preprocessing them with well-known undersampling and oversampling techniques. J48 (Java implementation of C4.5) is the base classifier used in all ensembles. The goal of the authors is to show that techniques for diversity increasing improve the performance of the ensemble methods for imbalance problems, but this does not always occur. Thus, metafeatures characterizing the complexity of the datasets are used in this study to know when its application is appropriate. However, features such as imbalance ratio are not considered.