## 2 Datasets, Feature Extraction, and Classification Methods
### Classification Methods
#### 2.3.1 Single Type Classifiers  
Two classification methodologies were applied: (1) Support Vector Machines (SVM); (2) Random Forests (RFs). SVM is a widely used classification method, in different and varied areas of research, partly because of its capability and good behavior when dealing with problems with a small number of samples in (relation to) high-dimensional feature spaces. Originally developed and applied in linearly separable problems, it is aimed at obtaining the hyperplane whose distance to the two groups of data points (called margin), representing the two classes, was maximal. SVM was generalized later to deal with nonlinearly separable problems using the so-called (transformational) Kernel trick [21]. A mathematical transformation function is applied to map the nonlinear separable dataset into a higher dimensional space where the samples can be linearly separated using an hyperplane.  
Under this mathematical framework, two parameters emerge \((C,\gamma)\). The optimal value of these parameters is problem dependent. A Grid Search strategy was applied to assess their optimal values. The parameter \(C\) search interval and step size was \(2^{-5},2^{-3},\ldots,\)\(2^{15}\), and the corresponding \(\gamma\) interval and step size was \(2^{-15}\), \(2^{-13},\ldots,\)\(2^{3}\). Whenever the best best pair of parameters was obtained in the interval limits, the Grid Search was automatically extended by a factor of two. This procedure follows the guidelines given in [34].  
Ensemble learning methods are based on the idea of using multiple learning algorithms to obtain better predictive performance than one could obtain from any of them, separately. The idea behind ensemble learning is the way an expert committee works in real life, i.e., it is usually easier to properly predict something when the prediction is made by more than one expert, and a consensus is obtained from them. Ensembles are combinations of several classifiers, which are often called base classifiers. There are several types of ensembles, which may be divided into two large groups: (a) homogeneous ensembles, where all the base classifiers are built using the same algorithm (but with different versions of the dataset or different training parameters); (b) heterogeneous ensembles, where the base classifiers are built using different algorithms.  
Diversity is a key property in the search for an optimal ensemble strategy performance, since there is no benefit when combining base classifiers that always obtain the same predictions. There are several techniques to induce diversity in homogeneous ensembles. In Bagging [35], for instance, each classifier is trained with a variant of the training dataset, which uses different random samples of the training set. Random Forests [20] are ensembles of Decision Trees [36]. In this method, the diversity during the training process is enforced by combining the sampling of the training set, as Bagging does, with the random selection of subsets of attributes in each node of the tree. This way, in each node, the splits only consider the selected subset of attributes. Later, on the prediction stage, each base classifier predicts a class, and the class selected the most (the mode) is the final prediction of the ensemble. RFs are used to correct the tendency of the decision trees to overfit. The main parameter of an RF is its size (i.e., the number of trees that are generated into the ensemble). In our study, 100 decision trees were used because it is a usual and sufficient number [37].