One-Class LP Classifiers for Dissimilarity Representations

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Elzbieta Pekalska, David M.J. Tax, Robert Duin


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.