Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance recent years. When class distribution imbalanced, classical machine learning algorithms tend to move strongly towards majority disregard minority. Therefore, accuracy may be high, but model cannot recognize instances minority classify them, leading many...