## 3 Materials and Methods
### Classification Algorithms  
In this study, several classification algorithms were applied. They were both simple and ensemble classifiers. As simple classifiers, we tested decision trees, Bayesian networks and SVM, although the results provided for the two last were remarkably poor and have not been reported. The ensembles applied were Random Tree, Random Forest, Bagging, AdaBoost and Random Committee. In addition, some neural network algorithms were tested, but they yielded worse results than simple classifiers, with the added drawback of the much longer computational time. Ensembles belong to the category of multiclassifiers, which combine several individual classifiers induced with different basic methods or obtained from different training datasets, with the aim of improving the accuracy of the predictions. They can be divided into two groups. The first, also named ensemble classifiers, such as bagging [43], boosting [44] and random forest [45], induce models that merge classifiers with the same learning algorithm, but introduce modifications in the training data set. The second type of methods, named hybrids, such as stacking [46] and cascading [47], create new hybrid learning techniques from different base-learning algorithms. Bagging is the acronym for Bootstrap AGGregatlNG. The method induces a multiclassifier that consists of an ensemble of single classifiers built on bootstrap replicates of the training set. Each classifier in the ensemble is trained with a set of examples generated randomly with replacement from the original training set, so that some examples may be repeated. Bagging uses a majority vote to combine the outputs of the classifiers in the ensemble. This procedure is an abstract level method, in which no information about the probability or correctness of the predicted labels is known. By contrast, other approaches, such as rank level or measurement level methods, provide rank and confidence information, respectively [48]. Boosting is a multiclassifier of the same kind as Bagging; however, this method assigns weights to the outputs of the induced single classifiers from different training sets (strategies). The weight of a strategy represents the probability of it being the most accurate of all. In an iterative process, the weightsare updated by increasing the weight of strategies with the correct prediction and reducing the weight of strategies with incorrect predictions. In this way, the multiclassifier is developed incrementally, adding one classifier at a time. The classifier that joins the ensemble at step k is trained on a data set selectively sampled from the training data set Z. The sampling distribution starts from uniform and progresses in each k step towards increasing the likelihood of worst classified data points at step k - 1. This algorithm is called AdaBoost and comes from ADAptive BOOSTing. This algorithm presents the advantage of driving the ensemble training error to zero in very few iterations [48]. Random forest [45] is an algorithm widely used in medical fields [49] that induces many decision trees, called random trees, each of which produces its own output for a given unclassified example. The most popular class obtained by simple vote is chosen as the final outcome. It is a variant of bagging where the induction of each tree is produced from a bootstrap sample obtained from the original data set, independently taking n examples with replacement, but with the same distribution for all trees in the forest. About one-third of the examples not used to build a tree, the out-of-bag (OOB) sample, are used as test set. Moreover, the (random) trees are built from a randomly selected subset S of M features from the original dataset of N features. The selection is carried out at each node of the tree, then the best feature in S to split the node is searched. The value of M suggested by [PERSON] is \(\left[\log_{2}N+1\right]\). In this way, CART (Classification and Regression Trees) is fully grown without pruning.