Batch and online learning algorithms for nonconvex neyman-pearson classification
Gilles Gasso, Aristidis Pappaioannou, M. A. Spivak, Léon Bottou · ACM Transactions on Intelligent Systems and Technology · 2011
We describe and evaluate two algorithms for Neyman-Pearson (NP) classification problem which has been recently shown to be of a particular importance for bipartite ranking problems. NP classification is a nonconvex problem involving a constraint on false negatives rate. We investigated batch algorithm based on DC programming and stochastic gradient method well suited for large-scale datasets. Empirical evidences illustrate the potential of the proposed methods.