1 research outputs found
On the Foundations of Adversarial Single-Class Classification
Motivated by authentication, intrusion and spam detection applications we
consider single-class classification (SCC) as a two-person game between the
learner and an adversary. In this game the learner has a sample from a target
distribution and the goal is to construct a classifier capable of
distinguishing observations from the target distribution from observations
emitted from an unknown other distribution. The ideal SCC classifier must
guarantee a given tolerance for the false-positive error (false alarm rate)
while minimizing the false negative error (intruder pass rate). Viewing SCC as
a two-person zero-sum game we identify both deterministic and randomized
optimal classification strategies for different game variants. We demonstrate
that randomized classification can provide a significant advantage. In the
deterministic setting we show how to reduce SCC to two-class classification
where in the two-class problem the other class is a synthetically generated
distribution. We provide an efficient and practical algorithm for constructing
and solving the two class problem. The algorithm distinguishes low density
regions of the target distribution and is shown to be consistent.Comment: 52 pages, 6 figure