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Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc.

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machine-learning smote-sampling class-imbalance cost-sensitive-learning ensemble-learning one-class-learning

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