A comparative study of the cascade-correlation architecture in pattern recognition applications

In this work, an experimental evaluation of the cascade-correlation architecture is carried out in different benchmarking pattern recognition problems. An extensive experimental framework is developed to establish a comparison between the cascade-correlation network (CC) and the more traditional multilayer perceptron (MLP) and radial basis function models (RBF). The different network configurations are evaluated with respect to generalization performance in three practical real-world tasks: the diagnosis of coronary diseases (heart), the credit screening problem (card) and the recognition of handwritten characters. It is also considered the issue of catastrophic forgetting in MLP and CC models. In addition to some clear potential advantages observed in the cascade-correlation network such as the on-learning definition of the number of hidden units, the practical satisfactory results obtained suggest that the CC model may represent in some situations an alternative to other traditional models such as the MLP and RBF networks