| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1181600 | Chemometrics and Intelligent Laboratory Systems | 2009 | 6 Pages |
An experimental strategy, based on a D-optimal design, to systematically study the influence of some metaparameters that affect the behaviour of a class-modelling method is described.The class-modelling method computes class-models by using neural networks trained by an evolutionary algorithm. The key is that the neural networks are trained to find a set of models that behave differently as regards sensitivity and specificity and that constitute the Pareto-optimal models for the class-modelling problem.A measure for comparing different Pareto-optimal fronts has been defined. In this way, by studying the effects from the D-optimal experimental design, the metaparameters that influence the behaviour of both neural networks and evolutionary algorithms when modelling a class are determined.As a case-study to explain the procedure, it has been applied to model the acceptance or rejection of 117 dry-cured ham samples based on their pastiness (a sensory property), using the NIR spectra (1050 variables) as predictor variables.
