# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 4 Results
### Santiago M4  
Table 7 shows the fifteen selected classifiers that satisfy the selection criterion (combination of preprocessing and classifier algorithms with AUC greater than 0.6).  
It stands out that the NNCS and the Bagging classifiers are present in almost all selected simple classifiers, and OSS in preprocessing algorithms. Nevertheless, these classifiers show a very low PPV. Therefore, the need of accomplishing the next step, ensembles generation, is justified. Ensembles with better metrics are shown in Table 8.  
It can be seen that PPV and \(S_{p}\) have improved markedly compared to simple classifiers, reaching values greater than 0.7 for the best ensemble. Global measures like F-Value and MCC have experimented a good improvement as well. Actually, average values for these parameters are above 0.7 and 0.5, respectively. In contrast, \(S_{n}\) has worsened (as expected) and GM is slightly lower (0.67 vs. 0.68), but still satisfactory.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Lidex** & **Preprocessing** & **Classifier** & **TP** & **FP** & **TN** & **FN** & \(\mathbf{S_{n}}\) & \(\mathbf{S_{p}}\) & **PPV** & **NPV** & **F** & **AUC** & **MCC** & **GM** \\ \hline
1 & SMOTE & NNCS & 12 & 57 & 91 & 0 & 1 & 0.62 & 0.17 & 1 & 0.53 & 0.81 & 0.33 & 0.78 \\
2 & OSS & OverBagging & 8 & 23 & 125 & 4 & 0.67 & 0.85 & 0.26 & 0.97 & 0.64 & 0.76 & 0.34 & 0.75 \\
3 & SMOTE\_TL & NNCS & 10 & 51 & 97 & 2 & 0.83 & 0.66 & 0.16 & 0.98 & 0.53 & 0.74 & 0.27 & 0.74 \\
4 & Safe\_Level\_SMOTE & NNCS & 11 & 67 & 81 & 1 & 0.92 & 0.55 & 0.14 & 0.99 & 0.47 & 0.73 & 0.24 & 0.71 \\
5 & OSS & OverBagging2 & 8 & 33 & 115 & 4 & 0.67 & 0.78 & 0.20 & 0.97 & 0.58 & 0.72 & 0.26 & 0.72 \\
6 & OSS & SMOTEBagging & 8 & 34 & 114 & 4 & 0.67 & 0.77 & 0.19 & 0.97 & 0.58 & 0.72 & 0.26 & 0.72 \\
7 & OSS & UnderOverBagging & 8 & 35 & 115 & 4 & 0.67 & 0.74 & 0.19 & 0.97 & 0.57 & 0.72 & 0.26 & 0.71 \\
8 & None & NNCS & 12 & 91 & 57 & 0 & 1 & 0.39 & 0.12 & 1 & 0.38 & 0.69 & 0.21 & 0.62 \\
9 & CPM & OverBagging2 & 8 & 46 & 102 & 4 & 0.67 & 0.69 & 0.15 & 0.96 & 0.52 & 0.68 & 0.20 & 0.68 \\
10 & SFIDER2 & UnderBagging & 7 & 35 & 113 & 5 & 0.58 & 0.76 & 0.17 & 0.96 & 0.55 & 0.67 & 0.21 & 0.67 \\
11 & CNNTL & OverBagging2 & 9 & 63 & 85 & 3 & 0.75 & 0.58 & 0.13 & 0.97 & 0.47 & 0.66 & 0.17 & 0.66 \\
12 & CNN & UnderBagging2 & 7 & 39 & 109 & 5 & 0.58 & 0.74 & 0.15 & 0.96 & 0.54 & 0.66 & 0.19 & 0.66 \\
13 & None & Balance\_Cascade & 9 & 64 & 84 & 3 & 0.75 & 0.57 & 0.12 & 0.97 & 0.46 & 0.66 & 0.17 & 0.65 \\
14 & OSS & RUBSonet & 12 & 102 & 46 & 0 & 1 & 0.31 & 0.11 & 1 & 0.33 & 0.66 & 0.18 & 0.56 \\
15 & AHC & EasyEnsemble & 6 & 30 & 118 & 6 & 0.50 & 0.80 & 0.17 & 0.95 & 0.56 & 0.65 & 0.19 & 0.63 \\ \hline  & Average & & 9 & 51.33 & 96.67 & 3 & 0.75 & 0.65 & 0.16 & 0.97 & 0.51 & 0.70 & 0.23 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fifteen imbalanced classifiers with Area Under the ROC Curve (AUC) \(>\) 0.6 in Santiago.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{4}{c}{**Training (50\%)**} & \multicolumn{4}{c}{**Test (50\%)**} \\ \cline{2-7}  & **Total** & **Positives** & **Negatives** & **Total** & **Positives** & **Negatives** \\ \hline Pichilemu\_M6 & 172 & 8 & 164 & 171 & 0 & 171 \\ Pichilemu\_M7 & 172 & 6 & 166 & 171 & 0 & 171 \\ Talca\_M5 & 102 & 2 & 100 & 102 & 5 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Positive and negative cases distribution in training and test sets with 50–50%.