Mathematical Programming Approaches to Classification Problems

Abstract EN

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 variance-covariance 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 Machines- (SVMs-) based approach.

The findings of this study will be useful in assisting decision-makers to choose the most appropriate model for their decision-making situation.