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

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 discriminant function (LDF) and Smith's quadratic 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.