Optimal Single-Class Classification Strategies

Abstract

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.

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Cite this paper

@inproceedings{ElYaniv2006OptimalSC, title={Optimal Single-Class Classification Strategies}, author={Ran El-Yaniv and Mordechai Nisenson}, booktitle={NIPS}, year={2006} }