# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 2 Related Works
In [12] an optimization model using different swarm strategies (Bat-inspired algorithm and PSO) is proposed for adaptively balancing the increase/decrease of the class distribution, depending on the properties of the biological datasets, which pretends to achieving the highest possible accuracy and Kappa statistics as well. Tested on five imbalanced medical datasets, that are sourced from lung surgery logs and virtual screening of bioassay data, results show that the proposed optimization model outperforms other class balancing methods in medical data classification. New re-sampling methods also appear in recent literature. A new oversampling method called Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord) is proposed in [13] for addressing ordinal regression with imbalanced datasets. It aims to address this problem by first clustering minority classes and then oversampling them based on their distances and ordering relationship to other classes' instances. Results demonstrate that the proposed CWOS-Ord method provides significantly better results compared to other methods based on the performance measures. Another algorithm, K Rare-class Nearest Neighbour (KRNN) is proposed in [14], by directly adjusting the induction bias of the k Nearest Neighbours (KNN). First, dynamic query neighbourhoods are formed, and to further adjust the positive posterior probability estimation to bias classification towards the rare class. Results showed that KRNN significantly improved KNN for classification of the rare class, and often outperformed re-sampling and cost-sensitive learning strategies with generality-oriented base learners. The occurrence of a minority class has also been forecasted by means of imbalanced classifiers, in the field of electricity [15]. The authors used different imbalanced approaches, combined with certain resampling methods, to forecast outliers in data from Spain's electricity demand. Reported results confirmed the usefulness of their approach. Imbalanced learning is present too on diagnosis of bearings, which generally plays an important role in fault diagnosis of mechanical system. An online sequential prediction method for imbalanced fault diagnosis problem based on extreme learning machine is proposed in [16], where under-sampling and over-sampling techniques plays again an important role. Using two typical bearing fault diagnosis data, results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms. Moreover, there are other approaches that applied imbalanced techniques to different data types, like images [17; 18], which involve high dimensional data resulting on robustness methods to address class imbalance. In conclusion, imbalanced learning has been widely studied in the literature, particularly in biology and clinical datasets. Additionally, a vast majority of approaches for earthquake prediction are focused on events of moderate magnitude. But the use of imbalanced classification for predicting large earthquakes is not reported in the state-of-the-art. It is then justified the need and novelty of using these techniques in this research field.