Combining nearest neighbor data description and structural risk minimization for one-class classification

One-class classification is an important problem with applications in several different areas such as novelty detection, anomaly detection, outlier detection and machine monitoring. In this paper, we propose two novel methods for one-class classification, referred to as NNDDSRM and kNNDDSRM. The methods are based on the principle of structural risk minimization and the nearest neighbor data description (NNDD) one-class classifier. Experiments carried out using both artificial and real-world datasets show that the proposed methods are able to significantly reduce the number of stored prototypes in comparison to NNDD. The experimental results also show that the proposed methods 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.