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PAC-Bayesian AUC classification and scoring
James Ridgway · Pierre Alquier · Nicolas Chopin · Feng Liang

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D

We develop a scoring and classification procedure based on the PAC-Bayesian approach and the AUC (Area Under Curve) criterion. We focus initially on the class of linear score functions. We derive PAC-Bayesian non-asymptotic bounds for two types of prior for the score parameters: a Gaussian prior, and a spike-and-slab prior; the latter makes it possible to perform feature selection. One important advantage of our approach is that it is amenable to powerful Bayesian computational tools. We derive in particular a Sequential Monte Carlo algorithm, as an efficient method which may be used as a gold standard, and an Expectation-Propagation algorithm, as a much faster but approximate method. We also extend our method to a class of non-linear score functions, essentially leading to a nonparametric procedure, by considering a Gaussian process prior.

Author Information

James Ridgway (Crest-Ensae and Dauphine)
Pierre Alquier (ENSAE)
Nicolas Chopin (CREST)
Feng Liang (Univ. of Illinois Urbana-Champaign)

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