Prototype-based Domain Description

In this work a novel one-class classifier, namely the Prototype-based Domain Description rule (PDD), is presented. The PDD classifier is equivalent to the NNDD rule under the infinity Minkowski metric for a suitable choice of the prototype set. The concept of PDD consistent subset is introduced and it is shown that computing a minimum size PDD consistent subset is in general not approximable within any constant factor. A logarithmic approximation factor algorithm, called the CPDD algorithm, for computing a minimum size PDD consistent subset is then introduced. The CPDD algorithm has some parameters which allow to tune the trade off between accuracy and size of the model. Experimental results show that the CPDD rule sensibly improves over the CNNDD classifier in terms of size of the subset, while guaranteeing a comparable classification quality.