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 is used to examine how the differe nt learning techniques perform with respect to certain data distribution ch aracteristics. Both linear and nonlinear discrimination methods are conside red. The data has been previously used in the comparative evaluation of a n umber of techniques and helps relate our findings across a range of discrim ination techniques.