One-class classifier based on extreme value statistics.
David Martínez‐Rego, Evan G. Kriminger, José Carlos Príncipe, Óscar Fontenla-Romero, Amparo Alonso‐Betanzos · The European Symposium on Artificial Neural Networks · 2012
In recent years, interest in one-class classification methods has soared due to its wide applicability in many practical problems in which classification in the absence of counterexamples is needed. In this paper, a new one class classification rule based on order statistics is presented. It only relies on embedding the classification problem into a metric space, so it is suitable for Euclidean or other structured mappings. The suitability of the proposed method is assessed through a comparison both for artificial and real life data sets. The good results obtained pave the road for its application on practical novelty detection problems.