An integrity constraint for database systems containing embedded static neural networks

Abstract

Static neural networks are used in some database systems to classify objects, but like traditional statistical classifiers they often misclassify. For some applications, it is necessary to bound the proportion of misclassified objects. This is clearly an integrity problem. We describe a new integrity constraint for database systems with embedded static neural networks, with which a database administrator can enforce a bound on the proportion of misclassifications in a class. The approach is based upon mapping probabilities generated by a probabilistic neural network to the likely percentage of misclassifications. © 2001 John Wiley & Sons, Inc.