System and method for the quality assurance of data-based models

Abstract

The invention relates to a system which, on the one hand, has a classifier that is formed by a discriminative neural network and that implements a binary class model or a multi-class model. On the other hand, the system has a model-based sample generator that is formed by a generative neural network. Both the classifier and the model-based sample generator are trained—for a corresponding class—with the same training data records and therefore embody models that correspond to one another for this class. The invention also relates to a method for determining a quality criterion for input data records for a classifier with a discriminative neural network. The classifier has been trained with training data records and represents a classification model for a class. According to the method, a model-based sample generator with a generative neural network is initially provided and trained with the same training data records that were used to train the classifier. Subsequently, by means of the trained model-based sample generator and an input data record based on random values, an artificial data record is generated, which is representative of the classification model embodied by the classifier. The artificial data record generated by the trained generator, or at least a parameter derived from it, is used to test the input data records as to their suitability for classification or regression.