## 2 Related Work
One-sided selection (OSS) is also a well-known undersampling technique [22] that uses k-NN to classify the instances of a subset of the training set initially containing all the examples of the minority class and randomly selected examples of the majority class. The majority class instances that are correctly classified are discarded because they are considered redundant. Noise and borderline instances are also removed. Due to its questionable effectiveness, especially when the imbalance rate is high, OSS is not usually used alone, but in combination with oversampling strategies [6]. There are some proposals in the literature that are mainly focused on minimizing the information loss caused by under-sampling. In [23], majority-class examples are separated into clusters that are later used to build different training subsets. Each subset is formed for all the minority class instances and as many majority class samples as clusters. These samples are proportional to the size of the clusters they belong to. The final output is given by the aggregation of the predicted results of individual classifiers. Since SVM (support vector machines) are one of the most suitable algorithms for binary classification, especially for high dimensionality problems, they have been extensively studied in imbalanced data contexts where they do not always have the desired behavior. However, an SVM can be adapted to generate an asymmetric margin, wider at the side of the minority class, in order to increase its performance [24; 25]. In [26], an evaluation of different well-known strategies for imbalanced text classification using SVM was carried out. The results showed that resampling and weighting strategies were not effective in that application domain. Soft-margin polynomial SVM combined with resampling methods have shown good performance in a study for classifying medical documents in which Unified Medical Language Systems (UMLS) were used for medical terms extraction [27]. Ensemble methods have also been applied to learn from imbalanced datasets, both alone and combined with other kinds of data sampling [5; 10; 11]. In [28], the imbalance problem is addressed by means of techniques originally created to increase the ensemble diversity. Some of the methods for the promotion of diversity expand the feature space with new attributes, but most of them are also based on sampling strategies. The study shows some improvement when using diversity techniques, but the results can be generalized neither to all problems considered nor for all quality measures. Diversity measures are used to prune base classifiers in the ensemble models in order to avoid overfitting, but the effects of diversity on accuracy are unclear. In addition, the pruning process requires the time-consuming task of training and evaluating many classifiers, which is a noteworthy drawback of these approaches [29]. One concern when using ensembles is the interpretability of the output,which becomes difficult due to the complexity of the models generated. In order to make them more understandable, a method is proposed in [30] that provides a single set of production rules by combining and simplifying the output of an ensemble of binary decision trees, without serious effects on performance. The problem of imbalanced data is very common in the medical field and has been addressed in some works, such as studies of mortality [12; 31; 32], treatment outcomes [33], drug toxicity assessment [34] and medical diagnosis [35; 36; 37]. Preliminary studies about the behavior of ensemble classifiers, as opposed to single classifiers in imbalanced data contexts, are conducted to predict the mortality of polytraumatized patients [12] and the success of non-invasive mechanical ventilation [33]. These works proved the good behavior of ensembles, even when applied to high-dimensionality datasets preprocessed with feature selection methods. In [38], several machine-learning algorithms are applied to predict the outcome of the implantation of individual embryos in in-vitro fertilization.