Optimal single-class classification strategies
Ran El-Yaniv and Mordechai Nisenson
In: NIPS 2006, 4-7 Dec 2006, Vancouver, Canada.
We consider single-class classification (SCC) as a two-person game between the learner and an adversary. In this game the target distribution is completely known to the learner and the learner's goal is to construct a classifier capable of guaranteeing
a given tolerance for the false-positive error while minimizing the false negative error. We identify both ``hard'' and ``soft'' optimal classification strategies for different types of games and demonstrate that soft classification can provide a significant advantage. Our optimal strategies and bounds provide worst-case lower bounds for standard, finite-sample SCC and also motivate new approaches to solving SCC.