V. Sinha

Learning Under Extreme Class Imbalance: A Comparative Study of Algorithmic and Data-Level Solutions

A systematic comparative framework that integrates data-level resampling, cost-sensitive learning, and hybrid approaches to evaluate their performance under varying imbalance ratios and noise levels indicates that hybrid approaches consistently outperform standalone methods, achieving the most stable and balanced perfo...