Decision Sciences Journal
Volume 29, Number 4
Fall 1998
Inductive, Evolutionary, and Neural Computing Techniques
for Discrimination: A Comparative Study
Siddhartha Bhattacharyya
Department of Information and Decision Sciences, College of Business
Administration, University of Illinois at Chicago, 601 South
Morgan Street, Chicago, IL 60607-7124,
email: sidb@uic.edu
Parag C. Pendharkar
Capital College, Pennsylvania State University, 777 W. Harrisburg
Pike, Middletown, PA 17057, email: pxp19@psu.edu
Abstract. This paper provides a comparative study of
machine learning techniques for two-group discrimination. Simulated
data is 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 has been previously used in the comparative evaluation
of a number of techniques and helps relate our findings across
a range of discrimination techniques.
Subject Areas: Discriminant Analysis, Genetic Algorithms,
Genetic Programming, Inductive Learning, Machine Learning, and
Neural Networks. |