Neyman-Pearson Criterion (NPC): A Model Selection Criterion for Asymmetric Binary Classification
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We propose a new model selection criterion, the Neyman-Pearson criterion (NPC), for asymmetric binary classification problems such as cancer diagnosis, where the two types of classification errors have vastly different priorities. The NPC is a general prediction-based criterion that works for most classification methods including logistic regression, support vector machines, and random forests. We study the theoretical model selection properties of the NPC for nonparametric plug-in methods. Simulation studies show that the NPC outperforms the classical prediction-based criterion that minimizes the overall classification error under various asymmetric classification scenarios. A real data case study of breast cancer suggests that the NPC is a practical criterion that leads to the discovery of novel gene markers with both high sensitivity and specificity for breast cancer diagnosis. The NPC is available in an R package NPcriterion.
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