On the Development of an Outranking Relation for Ordinal Classification Problems: An Experimental Investigation of a New Methodology

Classification problems involve the assignment of a discrete set of alternatives described over some criteria into predefined groups. This paper is focused on the case where the groups are defined in an ordinal way. The methodology proposed in the paper is based on the multicriteria decision aid (MCDA) approach and the outranking relations theory. A new approach is presented to determine the parameters of an outranking relation classification model using a training sample of alternatives. The proposed methodology is extensively validated in terms of its classification performance through a comprehensive Monte Carlo simulation. The results of the methodology are compared to linear, quadratic and logit analysis.