One-Class LP Classifiers for Dissimilarity Representations

Abstract

Problems in which abnormal or novel situations should be detected can be approached by describing the domain of the class of typical exam- ples. These applications come from the areas of machine diagnostics, fault detection, illness identification or, in principle, refer to any prob- lem where little knowledge is available outside the typical class. In this paper we explain why proximities are natural representations for domain descriptors and we propose a simple one-class classifier for dissimilarity representations. By the use of linear programming an efficient one-class description can be found, based on a small number of prototype objects. This classifier can be made (1) more robust by transforming the dissimi- larities and (2) cheaper to compute by using a reduced representation set. Finally, a comparison to a comparable one-class classifier by Campbell and Bennett is given.