Input space configuration effects in neural network-based grade estimation
The way input data are presented to Artificial Neural Networks is one of the most important parameters controlling their performance during the development and application stages. The choice of dimensions that form the input space of a network (dimensionality) is very important and must be investigated as to its effects on the performance of artificial neural network systems applied to grade estimation. The study of these effects was achieved by configuring the available data in order to form different multidimensional input spaces and testing on different datasets. The results obtained from the numerous tests in this study lead to a better understanding of the behaviour of artificial neural network systems when facing different input space configurations using the same data, and aid the choice of dimensions that will allow better representation of samples for their development for grade estimation.
neural networks grade estimation dimensionality spatial interpolation radial basis functions