Inductive, evolutionary, and neural computing techniques for discrimination: A comparative study

This paper provides a comparative study of machine learning techniques for two-group discrimination. Simulated data are used to examine how the different learning techniques perform with respect to certain data distribution characteristics. Both linear and nonlinear discrimination methods are considered. The data have been previously used in the comparative evaluation of a number of techniques and help relate our findings across a range of discrimination techniques.