Abstract
Among the various discriminant analysis (DA) methods, researchers hav e investigated several directions in this area: statistics, econometrics, computer data mining technologies and mathematical programming. Recently, as a nonparametric mathematical programming approach, Data envelopment analysis has been applied in DA area and received great attention. In this paper, we propose a new discriminant approach based upon the relative distance measured by super-efficiency data envelopment analysis (DEA). This approach may generally avoid the drawbacks that usually occur in statistics discriminations of constructing function to determine a DMU’s category. On the other hand, this approach may maintain discriminant capabilities by incorporating the non-parametric feature of DEA into DA. At the same time, it can also inherit the advantages of avoiding the process of dealing with different dimensional data in DEA. Our approach can be used to classify a sample’s category by the discrimination results, even in the multiple-groups situation. Therefore, it can be applied to the discriminant analysis in various real-life cases.
Highlights
IntroductionDiscriminant analysis (DA) model can be described as: there are k categories, G1,..., Gk
Discriminant analysis (DA) model can be described as: there are k categories, G1,..., Gk .Given a new sample, we should determine which category it belongs to
data envelopment analysis (DEA)-DA approach is a type of non-parametric DA approach that provides a set of weights of a piecewise linear discriminant function(s), and yields an evaluation score(s) for the determination of group membership (Sueyoshi, 2006)
Summary
Discriminant analysis (DA) model can be described as: there are k categories, G1,..., Gk. Data envelopment analysis (DEA) is a non-parametric programming technique for evaluating the relative efficiency of a set of homogenous decision making units (DMUs) with multiple inputs and multiple outputs. It has been applied in many areas, such as schools, hospitals, shops, bank branches and so on (Wu et al, 2010; Ozgen et al, 2011; Lopez et al, 2010; Doreswamy, 2010; Xu et al, 2009 and Kaya, 2010). DEA-DA approach is a type of non-parametric DA approach that provides a set of weights of a piecewise linear discriminant function(s), and yields an evaluation score(s) for the determination of group membership (Sueyoshi, 2006). Based on the super-efficiency DEA method, we measure relative distance of the new DMU to the best-practice and the worst-practice frontier, and determine its category according some rules
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
