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Found 3 papers, 1 papers with code
Influence of Resampling on Accuracy of Imbalanced Classification
In many real-world binary classification tasks (e. g. detection of certain objects from images), an available dataset is imbalanced, i. e., it has much less representatives of a one class (a minor class), than of another.
Model Selection for Anomaly Detection
Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e. g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc.
Meta-Learning for Resampling Recommendation Systems
One possible approach to tackle the class imbalance in classification tasks is to resample a training dataset, i. e., to drop some of its elements or to synthesize new ones.
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