DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches

Discriminant Analysis (DA) is a classification method that can predict the group membership of a newly sampled observation. Recently, a new type of non-parametric DA approach is proposed to provide a set of weights of a discriminant function, consequently yielding an evaluation score for the determination of group membership. The non-parametric DA is referred to as “Data Envelopment Analysis-Discriminant Analysis (DEA-DA),” because it maintains its discriminant capabilities by incorporating the non-parametric feature of DEA into DA. In this study, a use of the mixed integer approach of DEA-DA is compared with other DA methods. It is confirmed that it performs at least as well as the other well known DA methods. The proposed approach is further reformulated in a manner that it can deal with classification of more than two groups.