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The Scientific World Journal
Volume 2014 (2014), Article ID 730712, 7 pages
http://dx.doi.org/10.1155/2014/730712
Research Article

Towards Application of One-Class Classification Methods to Medical Data

1Department of Computer Sciences and Artificial Intelligence, UPV/EHU, 20018 Donostia, Spain
2Department of Statistics, UB, 08028 Barcelona, Spain

Received 5 December 2013; Accepted 24 February 2014; Published 20 March 2014

Academic Editors: V. Bhatnagar and Y. Zhang

Copyright © 2014 Itziar Irigoien et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.