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What is Inverse Random Under Sampling Method about?

The document presents a new method called inverse random under sampling (IRUS) to address class imbalance problems in machine learning. IRUS severely under samples the majority class to create multiple training subsets where the minority class outnumbers the majority class. A classifier is trained on each subset to learn the decision boundary between the classes. The individual classifiers are then combined through fusion to produce a composite decision boundary for improved classification of imbalanced dat

Author

Zain Ul Haq

Language

EN