# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 3 Methodology  
The methodology designed is carried out as follows: given a dataset, this is rebalanced by applying a preprocessing algorithm. Then, a classification algorithm is applied to the rebalanced dataset in order to obtain an accurate result. In this methodology, all the classifiers created in this first stage are named as simple classifiers since they are created by using only one classification algorithm and one (or none) preprocessing algorithm. This is only a nomenclature, since it is possible that some classification algorithms used internally contain some preprocessing. Figure 1 illustrates the general procedure. As a first stage, the goal is to obtain as many simple classifiers as possible combinations between preprocessing and classific  
Figure 1: General flowchart of the proposed methodology. The approaches proposed to solve imbalanced classification problems can be split into two differentiated groups: algorithm-based approaches that design specific algorithms to deal with the minority class, and data-based approaches, which apply a preprocessing step to try to balance the classes before applying a learning algorithm [2]. In this work, a selection of representative methods of the first group are firstly used, and thereafter, the algorithm with the best performance will be combined with different preprocessing methods in order to improve the results of the predictions. Table 1 shows both preprocessing and classification techniques that have been used. Due to the good behavior exhibited in oversampling methods [2], a number of oversampling-based preprocessing techniques greater than that ones based on undersampling has been tested. All these techniques can be found in the KEEL open source java software project [19]. Once all simple classifiers are created and evaluated, the best of them are selected so as to be used in the second stage, where ensembles are generated. The selection criterion of the best simple classifiers of a dataset consists in selecting the 15 classifiers with the highest Area Under the ROC Curve (AUC) as long as these conditions are satisfied:  
* The AUC is greater than 0.6 in order to avoid selecting bad classifiers that could hinder the generation of good ensembles. In case of having less than 15 classifiers with an AUC greater than 0.6, select only those fulfilling this criterion. * There are not selected classifiers with equal AUC in order to avoid selecting identical classifiers that would generate redundant ensembles. In case of having two or more classifiers with equal AUC, randomly select only one of them and continue selecting the next classifiers with the highest AUC. After having selected the best simple classifiers, the methodology generates the ensembles in the second stage of the methodology so as to obtain classifiers which could improve the performance of the simple classifiers. Ensembles are developed to combine several classifiers' outputs. In this sense, they can be designed for two different purposes: (1) to increase sensitivity (2) to increase specificity. If option (1) is desired then an OR ensemble is the most suitable one, since an \"1\" is predicted if at least one classifier predicts an \"1\". By contrast, if option (2) is desired then an AND ensemble must be applied. Given the nature of the problem, the second option has been chosen for this work. These ensembles are the result of the intersection between the predictions of the simple classifiers which intervene in the generation of them. The intersection of different predictions reduces the number of false positives, that is, avoids to predict that a large earthquake will occur for the next five days and it does not really occur. Obviously, a decrease of the large earthquakes properly predicted by the classification technique is also expected (a sensitivity reduction) but, as discussed in [38], triggering