A Novel One-Class Classification Method Based on Feature Analysis and Prototype Reduction

One-class classification is an important problem with applications in several different areas such as outlier detection and machine monitoring. In this paper we propose a novel method for one-class classification which also implements prototype reduction. The main feature of the proposed method is to analyze every limit of all the feature dimensions to find the true border which describes the normal class. To this end, the proposed method simulates the novelty class by creating artificial prototypes outside the normal description. The method is able to describe data distributions with complex shapes. Aiming to assess the proposed method, we carried out experiments with synthetic and real datasets to compare it with the Support Vector Domain Description (SVDD), kMeansDD, ParzenDD and kNNDD methods. The experimental results show that our one-class classification approach outperformed the other methods in terms of the area under the receiver operating characteristic (ROC) curve in three out of six data sets. The results also show that the proposed method remarkably outperformed the SVDD regarding training time and reduction of prototypes.