A Novel Method for One-Class Classification Based on the Nearest Neighbor Data Description and Structural Risk Minimization

One-class classification is an important problem with applications in several different areas such as novelty detection, outlier detection and machine monitoring. In this paper we propose a novel method for one-class classification, referred to as NNDDSRM. It is based on the principle of structural risk minimization and the nearest neighbor data description (NNDD) method. Experiments carried out using both artificial and real-world datasets show that the proposed method is able to significantly reduce the number of stored prototypes in comparison to NNDD. The experimental results also show that the proposed method outperformed NNDD - in terms of the area under the receiver operating characteristic (ROC) curve - on four of the five datasets considered in the experiments and had a similar performance on the remaining one.

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