Neyman-Pearson Classification, Convexity and Stochastic Constraints

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i) its probability of type I error is below a pre-specified level and (ii), it has probability of type II error close to the minimum possible. The proposed classifier is obtained by solving an optimization problem with an empirical objective and an empirical constraint. New techniques to handle such problems are developed and have consequences on chance constrained programming. keywords: binary classification, Neyman-Pearson paradigm, anomaly detection, stochastic constraint, convexity, empirical risk minimization, chance constrained optimization.