Journal of Applied Mathematics and Decision Sciences
Volume 1 (1997), Issue 1, Pages 45-51

Effect of prior probabilities on the classificatory performance of parametric and mathematical programming approaches to the two-group discriminant problem

Constantine Loucopoulos

Box 4023, School of Business, Emporia State University, Emporia 66801, KS, USA

Copyright © 1997 Constantine Loucopoulos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A mixed-integer programming model (MIP) incorporating prior probabilities for the two-group discriminant problem is presented. Its classificatory performance is compared against that of Fisher's linear discrimininant function (LDF) and Smith's quadradic discriminant function (QDF) for simulated data from normal and nonnormal populations for different settings of the prior probabilities of group membership. The proposed model is shown to outperform both LDF and QDF for most settings of the prior probabilities when the data are generated from nonnormal populations but underperforms the parametric models for data generated from normal populations.