Training data development with the D-optimality criterion

Abstract

The importance of using optimum experimental design (OED) concepts when selecting data for training a neural network is highlighted in this paper. We demonstrate that an optimality criterion borrowed from another field; namely the D-optimality criterion used in OED, can be used to enhance the training value of a small training data set. This is important in cases where resources are limited, and collecting data is expensive, hazardous, or time consuming. The analysis results in the cases considered indicate that even with a small set of training examples, so long as the training data set was chosen according to the D-optimality criterion, the network was able to generalize, and as a result, was able to fit complex surfaces.