Rigorous and compliant approaches to one-class classification

- Many qualitative analytical issues should be addressed by one-class classification.
- We present 'rigorous' and 'compliant' approaches for model development.
- In the 'rigorous' approach, only information from the target class is employed.
- In the 'compliant' approach, the non-target class data is also utilised.
- The pros and cons of both methods are critically discussed.

A wide number of real problems requiring qualitative answers should be addressed by one-class classification (OCC), as in the case of authentication studies, verification of particular claims and quality control. The key feature of OCC is that models are developed using only samples from the target class, so that a representative sampling is not strictly required for non-target classes. On the contrary, in the discriminant analysis (DA) approach, all of the classes considered (at least two) have a non-negligible influence in the definition of the delimiter. It follows that faults in the definition of the classes involved and in representative sampling for each of them may determine a bias in the classification rules. A key aspect in one-class classification concerns model optimisation. When the optimal modelling conditions are searched by considering parameters such as type II error or specificity ('compliant' approach), information from the non-target class is being used and may therefore determine a bias in the model. In order to build pure class models ('rigorous' approach), only information from the target class should be regarded: in other words, optimisation should be performed only considering type I error, or sensitivity. In the present study, 'compliant' and 'rigorous' approaches are critically compared on real case studies, by applying two novel modelling techniques: partial least squares density modelling (PLS-DM) and data driven soft independent modelling of class analogy (DD-SIMCA).