Neyman Pearson

Neyman-Pearson (NP) classification focuses on optimizing classifiers by controlling one type of error (e.g., false positives) while minimizing the other, addressing the limitations of minimizing overall error in imbalanced datasets. Current research emphasizes extending NP methods to handle complex scenarios like multi-class problems, transfer learning with limited outlier data, and the presence of missing or noisy data, often employing adaptations of existing algorithms like KLIEP or cost-sensitive learning. These advancements are crucial for applications where error asymmetry is critical, such as medical diagnosis and cybersecurity, enabling more robust and reliable decision-making in high-stakes situations.