Jingyi Jessica Li

Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification

2020 · arXiv (Cornell University)

Asymmetric binary classification problems, in which the type I and II errors have unequal severity, are ubiquitous in real-world applications. To handle such asymmetry, researchers have developed the cost-sensitive and Neyman-Pearson paradigms for training classifiers …