Novelty Detection Based on Genuine Normal and Artificially Generated Novelty Examples

One-class classification (OCC) is an important problem with applications in several different areas such as outlier detection and machine monitoring. Since in OCC there are no examples of the novelty class, the description generated may be a tight or a bulky description. Both cases are undesirable. In order to create a proper description, the presence of examples of the novelty class is very important. However, such examples may be rare or absent during the modeling phase. In these cases, the artificial generation of novelty samples may overcome this limitation. In this work it is proposed a two steps approach for generating artificial novelty examples in order to guide the parameter optimization process. The results show that the adopted approach has shown to be competitive with the results achieved when using real (genuine) novelty samples.