Article ID: | iaor20105108 |
Volume: | 2009 |
Issue: | 1 |
Start Page Number: | 1 |
End Page Number: | 10 |
Publication Date: | Feb 2010 |
Journal: | Advances in Operations Research |
Authors: | Chabchoub Habib, Aouni Belaid, Smaoui Soulef |
Keywords: | classification |
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.