# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 4 Results
### Talca M5  
Table 11 shows the fifteen selected classifiers following the selection criterion described in Section 3. Overall, simple classifiers obtained quite good measures, except for PPV, which is a critical measure in the matter at hand. This is due to the high FP/TP rate reached.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Ensemble** & **TP** & **FP** & **TN** & **FN** & \(S_{n}\) & \(S_{p}\) & **PPV** & **NPV** & **F** & **AUC** & **MCC** & **GM** \\ \hline \(1,2,3,4,6\) & 17 & 29 & 263 & 18 & 0.48 & 0.90 & 0.37 & 0.94 & 0.67 & 0.69 & 0.34 & 0.66 \\ \(1,2,3,4,5,6\) & 16 & 28 & 264 & 19 & 0.46 & 0.90 & 0.36 & 0.93 & 0.66 & 0.68 & 0.33 & 0.64 \\ \(1,3,4,6\) & 18 & 32 & 260 & 17 & 0.51 & 0.89 & 0.36 & 0.94 & 0.67 & 0.70 & 0.35 & 0.68 \\ \(1,2,3,5,6\) & 19 & 34 & 258 & 16 & 0.54 & 0.88 & 0.36 & 0.94 & 0.67 & 0.71 & 0.36 & 0.69 \\ \(1,3,4,5,6\) & 17 & 31 & 261 & 18 & 0.49 & 0.89 & 0.35 & 0.94 & 0.66 & 0.69 & 0.33 & 0.66 \\ \(1,2,3,6\) & 20 & 37 & 255 & 15 & 0.57 & 0.87 & 0.35 & 0.94 & 0.67 & 0.72 & 0.36 & 0.71 \\ \(1,2,4,6\) & 17 & 32 & 260 & 18 & 0.49 & 0.89 & 0.35 & 0.94 & 0.66 & 0.69 & 0.33 & 0.66 \\ \(1,3,6\) & 21 & 40 & 252 & 14 & 0.60 & 0.86 & 0.34 & 0.95 & 0.67 & 0.73 & 0.37 & 0.72 \\ \hline Average & 18.13 & 32.88 & 259.13 & 16.88 & 0.52 & 0.89 & 0.36 & 0.94 & 0.67 & 0.70 & 0.35 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ensembles generated for Valparaiso.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Ensemble** & **TP** & **FP** & **TN** & **FN** & \(S_{n}\) & \(S_{p}\) & **PPV** & **NPV** & **F** & **AUC** & **MCC** & **GM** \\ \hline \(4,5,7,8,10,11,12,14,15\) & 5 & 2 & 146 & 7 & 0.42 & 0.99 & 0.71 & 0.95 & 0.75 & 0.70 & 0.52 & 0.64 \\ \(1,4,8,10,11,14,15\) & 6 & 3 & 145 & 6 & 0.50 & 0.98 & 0.67 & 0.96 & 0.77 & 0.74 & 0.55 & 0.70 \\ \(2,4,5,7,8,11,12,14,15\) & 5 & 3 & 145 & 7 & 0.42 & 0.98 & 0.63 & 0.95 & 0.73 & 0.70 & 0.48 & 0.64 \\ \(2,4,5,7,8,10,11,12,14\) & 6 & 4 & 144 & 6 & 0.50 & 0.97 & 0.60 & 0.96 & 0.76 & 0.74 & 0.52 & 0.70 \\ \hline Average & 5.50 & 3 & 145 & 6.50 & 0.46 & 0.98 & 0.65 & 0.96 & 0.75 & 0.72 & 0.52 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ensembles generated for Santiago.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Index** & **Preprocessing** & **Classifier** & **TP** & **FP** & **TN** & **FN** & \(S_{n}\) & \(S_{p}\) & **PPV** & **NPV** & **F** & **AUC** & **MCC** & **GM** \\ \hline \(1\) & CNN & UnderBagging & 27 & 107 & 185 & 8 & 0.77 & 0.63 & 0.2 & 0.96 & 0.54 & 0.70 & 0.25 & 0.70 \\ \(2\) & CNN & EasyEnsemble & 30 & 151 & 141 & 5 & 0.86 & 0.48 & 0.17 & 0.97 & 0.46 & 0.67 & 0.21 & 0.64 \\ \(3\) & SMOTE & NNCS & 30 & 155 & 137 & 5 & 0.86 & 0.47 & 0.16 & 0.96 & 0.45 & 0.66 & 0.20 & 0.63 \\ \(4\) & Safe Level & NNCS & 29 & 151 & 141 & 6 & 0.83 & 0.48 & 0.16 & 0.96 & 0.46 & 0.66 & 0.19 & 0.63 \\ \(5\) & CNNTL & BalanceCascade & 32 & 177 & 115 & 3 & 0.91 & 0.39 & 0.15 & 0.97 & 0.41 & 0.65 & 0.20 & 0.60 \\ \(6\) & Borderline & NNCS & 25 & 142 & 150 & 10 & 0.71 & 0.51 & 0.94 & 0.46 & 0.61 & 0.14 & 0.61 \\ \(7\) & TL & NNCS & 26 & 157 & 135 & 9 & 0.74 & 0.46 & 0.14 & 0.94 & 0.43 & 0.60 & 0.13 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Seven imbalanced classifiers with AUC \(>\) 0.6 in Valparaiso.  
Once simple classifiers with good performance are identified (AUC \(>\) 0.6), ensembles composed of two simple classifiers are generated. It has been found that some ensembles reported no FP nor FN. Obviously, this fact leads to perfect values for all the considered quality measures, and therefore, new ensembles with a bigger number of simple classifiers have not been generated. These results are shown in Table 12. In short, although simple classifiers exhibited good performances, they are not considered reliable enough classifiers because of their low PPV. Ensembles generation have solved this problem, reaching perfect classification with some ensembles.