Project outcome classification with imprecise criteria information

A case in which managers have to make project outcome classification decisions with uncertainty in independently related criteria values is considered in this paper. A multi-criteria decision model is developed in this paper by selecting methods which delved into data analysis to help managers make informed classification decisions. Uncertainty in the criteria values is resolved using linear programming which enables managers to know the profit outcome of their projects for efficient resource allocation. The classification scheme from the linear programming process is used as predefined classification inputs for use in the Utilities Additives Discriminants(UTADIS) method, which further produces a classification model. The analysis presented a no misclassification error in the predefined classifications from the linear programming and the classifications in the UTADIS method, thus further boosting the confidence managers can entrust in the resulting classification model