Selecting the neural network topology for student modelling of prediction of corporate bankruptcy

Abstract

Neural network topology selection refers to a systematic procedure for selecting between competing models. Naturally, it is regarded as a key aspect in optimisation and replicability of neural network performance. When constructing neural network topologies, it is necessary to determine from the outset the general taxonomy of the neural network architectures to be constructed. The taxonomy considered in this study is the general taxonomy of time‐varying patterns which subsumes many existing architectures in the literature and points to several promising neural network architectures that have yet to be examined. The context of the problem is that choosing the right neural network topology for use in a particular domain such as corporate bankruptcy prediction with optimum generalisation performance is not, in any case, a trivial problem. The results of experiments presented in this paper would serve as a baseline against which to select between two competing architectures.