One of the limiting factors in training data-driven, rare-event prediction algorithms is scarcity events interest resulting an extreme imbalance data. There have been many methods introduced literature for overcoming this issue; simple data manipulation through undersampling and oversampling, utilizing cost-sensitive learning algorithms, or by generating synthetic points following distribution ...
