# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 3 Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Preprocessing**} & \multicolumn{3}{c}{**Classification**} \\ \hline
**Algorithm** & **Type** & **Reference** & **Algorithm** & **Reference** \\ \hline ADASYN & Oversampling & [20] & AdaBoost & [21] \\ ADOMS & Oversampling & [22] & AdaBoostM1 & [23] \\ ROS & Oversampling & [24] & AdaBoostM2 & [25] \\ Safe-Level-Smote & Oversampling & [26] & Bagging & [27] \\ SMOTE & Oversampling & [28] & BalanceCascade & [29] \\ SMOTE-ENN & Oversampling & [24] & C45 CS & [30] \\ SMOTE-TL & Oversampling & [24] & C-SVMCS & [31] \\ SPIDER & Oversampling & [32] & DataBoost-IM & [33] \\ SPIDER2 & Oversampling & [26] & EasyEnsemble & [29] \\ CNN & Undersampling & [34] & UnderBagging & [35] \\ CNNTL & Undersampling & [36] & UnderBagging2 & [35] \\ TL & Undersampling & [36] & UnderOverBagging & [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Techniques of imbalanced classification. a false alarm is quite an undesirable situation. Table 2 illustrates an example about how the prediction is made when three models are considered. Only if all the three models agree in assigning a \"1\" to the sample to be classified, \"1\" is assigned. In this case, \"1\" would mean that a large earthquake will occur for the next five days. The ensembles generation is done in an iterative way. First, all ensembles from two simple classifiers are generated; then, three simple classifiers are used to generate the ensembles and so until either all existing ensembles from simple classifiers selected in the previous stage are generated or an tensemble whose performance meets expectations it is found. During the generation of ensembles, if the probability of correctly predicting a large earthquake is equal to one for an ensemble then new ensembles are not generated from this one. Thus, generating useless classifiers whose performance do not improve the best classifier found so far is avoided. It can be noted that the main consequence of computing the final forecasting by using the intersection of the predictions between some classifiers is to create more conservative classifiers with less false positives and a higher probability of correctly predicting large earthquakes, in exchange of a less number of large earthquakes properly predicted. Therefore, if an ensemble is generated from another ensemble that predicts correctly large earthquakes with a probability of one, there is no room for improvement and it is not possible to find a new ensemble that reduces the number of false positives and increases this probability, making useless this generation. After generating, evaluating and selecting the best ensembles, the methodology is concluded. The flowchart describing the full methodology in shown in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model 1** & **Model 2** & **Model 3** & **Ensemble** \\ \hline
1 & 1 & 0 & 0 \\
1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Illustrative example of the proposed ensemble, in order to reduce false positives. Figure 2: Proposed methodology, based on imbalanced classifiers and ensemble learning.