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The aim of this paper is to propose a new approach, based on fuzzy goal programming, for classification problems where the cut-off value c corresponding to the discriminant axe is considered as imprecise. The fuzziness was handled through different membership functions. The proposed model will be illustrated through two and multi-groups 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 Machines-SVMs-based approach. The findings of this study will be useful in assisting decisionmakers to choose the most appropriate model for their decision-making situation.

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