An experimental evaluation of neural networks for classification

Artificial neural networks are new methods for classification. In this paper, the authors describe how to build neural network models. These models are then compared with classical models such as linear discriminant analysis and quadratic discriminant analysis. While neural network models can solve some difficult classification problems where classical models cannot, the results show that even under best conditions for the classical models, neural networks are quite competitive. Furthermore, neural networks are more robust in that they are less sensitive to changes in sample size, number of groups, number of variables, proportions of group memberships, and degrees of overlap among groups.