A Modified Super-Efficiency DEA Approach for Solving Multi-Groups Classification Problems

DOI

10.2991/ijcis.2011.4.4.17How to use a DOI?

Keywords

šData Envelopment Analysis (DEA); Super-efficiency; Discriminant analysis; Relative distance

Abstract

Among the various discriminant analysis (DA) methods, researchers have 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.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jie Wu
AU  - Qingxian An
AU  - Liang Liang
PY  - 2011
DA  - 2011/07/05
TI  - A Modified Super-Efficiency DEA Approach for Solving Multi-Groups Classification Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 606
EP  - 618
VL  - 4
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.4.17
DO  - 10.2991/ijcis.2011.4.4.17
ID  - Wu2011
ER  -