Data envelopment analysis with 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.