# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 4 Results
### Datasets Description and Generation  
The target zones are those introduced in [38]. In it, the authors studied four regions in Chile Santiago, Valparaiso, Tala and Pichilemu. However, before applying the methodology to all datasets, they must be analyzed in order to select only those which could be useful for the application of imbalanced classifiers. Note that the class indicates whether an earthquake with magnitude larger than a preset threshold is occurring within the next five days (label \"1\") or not (label \"0\"). In the original work, the authors set this threshold so that datasets were not imbalanced.  
Therefore, first of all and for every dataset, it is shown in Table 3 the total number of cases, as well as positive and negative cases from each one of them. Besides, it is shown the ratio of positive cases over total cases, which determines how imbalanced the dataset is. \(Mx\), with \(x=4,5,6,7\) means that the class has been assigned considering magnitudes greater than x. For instance, Pichilemu_M4 indicates data from the region of Pichilemu, with binary classes showing magnitudes larger than 4.0.  
Second, some datasets are discarded. In particular, all datasets without positive cases are discarded. Additionally, all datasets which are not imbalanced (positive cases \(<\)15%) are discarded as well.  
As the evaluation technique used is the standard holdout, with 66% of cases in training set and 34% of cases in test set, it is shown in Table 4 how positive and negative cases are distributed in training and test sets, respectively, for every dataset.  
All datasets without positive cases in the test set are discarded, remaining only two datasets to which the proposed methodology can be applied (Santiago_M4 and Valparaiso_M5). In order to use some of the discarded datasets and enlarge the experimentation, it is shown in Table 5 how positive and negative cases would be distributed if an alternative holdout (50% of cases in training set and 50% of cases in test set) would be used on all those discarded datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{3}{c}{**Training (66\%)**} & \multicolumn{3}{c}{**Test (34\%)**} \\ \cline{2-6}  & **Total** & **Positives** & **Negatives** & **Total** & **Positives** & **Negatives** \\ \hline Pichilemu\_M6 & 228 & 8 & 220 & 115 & 0 & 115 \\ Pichilemu\_M7 & 228 & 6 & 222 & 115 & 0 & 115 \\ Talca\_M5 & 136 & 7 & 129 & 68 & 0 & 68 \\ Santiago\_M4 & 320 & 9 & 311 & 160 & 12 & 148 \\ Valparaiso\_M5 & 652 & 7 & 645 & 327 & 35 & 292 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Positive and negative cases distribution in training and test sets with 66–34%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Total** & **Positives** & **Negatives** & **Imbalance** \\ \hline Pichilemu\_M4 & 343 & 211 & 132 & 61.52\% \\ Pichilemu\_M5 & 343 & 122 & 221 & 35.57\% \\ Pichilemu\_M6 & 343 & 8 & 335 & 2.33\% \\ Pichilemu\_M7 & 343 & 6 & 337 & 1.75\% \\ Talca\_M4 & 204 & 69 & 135 & 33.82\% \\ Talca\_M5 & 204 & 7 & 197 & 3.43\% \\ Talca\_M6 & 204 & 0 & 204 & 0 \\ Talca\_M7 & 204 & 0 & 204 & 0 \\ Santiago\_M4 & 480 & 21 & 459 & 4.38\% \\ Santiago\_M5 & 480 & 0 & 480 & 0 \\ Santiago\_M6 & 480 & 0 & 480 & 0 \\ Santiago\_M7 & 480 & 0 & 480 & 0 \\ Valparaiso\_M4 & 979 & 166 & 813 & 16.96\% \\ Valparaiso\_M5 & 979 & 42 & 937 & 4.29\% \\ Valparaiso\_M6 & 979 & 0 & 979 & 0 \\ Valparaiso\_M7 & 979 & 0 & 979 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Imbalance ratio for all datasets initially considered.  
Considering this new distribution, one of the three previously discarded datasets can be eventually used (Talca_M5). Finally, Table 6 shows the selected datasets and which evaluation technique has been used for each one.  
Please note that all processed datasets will be available upon acceptance.