Mathematical Programming Approaches to Classification Problems

Discriminant Analysis DA is widely applied in many fields. Some recent researches raise the fact that standard DA assumptions, such as a normal distribution of data and equality of the variancecovariance matrices, are not always satisfied. A Mathematical Programming approach MP has been frequently used in DA and can be considered a valuable alternative to the classical models of DA. The MP approach provides more flexibility for the process of analysis. The aim of this paper is to address a comparative study in which we analyze the performance of three statistical and some MP methods using linear and nonlinear discriminant functions in two-group classification problems. New classification procedures will be adapted to context of nonlinear discriminant functions. Different applications are used to compare these methods including the Support Vector MachinesSVMsbased approach. The findings of this study will be useful in assisting decisionmakers to choose the most appropriate model for their decision-making situation.