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What is Cost-Sensitive Learning for Imbalanced Data about?

This technical report discusses cost-sensitive learning (CSL) as a solution for imbalanced data classification, where traditional algorithms often favor the majority class. It outlines the theoretical foundations, methodologies, evaluation metrics, and case studies demonstrating the effectiveness of CSL in applications like fraud detection and medical diagnosis. The report concludes with potential research directions to further explore and enhance cost-sensitive learning techniques.

Author

priyanshukaloya2002

Language

EN