A Gram-Schmidt process based approach for improving DEA discrimination in the presence of large dimensionality of data set

Expert Systems with Applications

Volumn 39, Issue 3, 2012, Pages 3793-3799

  Bian, Yiwen ^a  

^a SHANGHAI UNIVERSITY   (China)

Author keywords

Data envelopment analysis (DEA); Dimension reduction; Gram Schmidt process; Multiple correlations; Principal component analysis (PCA)

Indexed keywords

ASSURANCE REGION; DATA DIMENSIONS; DATA SETS; DEA MODELS; DIMENSION REDUCTION; ECONOMIC PERFORMANCE; GRAM-SCHMIDT; MAXIMUM VARIANCE; MULTIPLE CORRELATION; PERFORMANCE EVALUATION; PRINCIPAL COMPONENT ANALYSIS (PCA); PRINCIPAL COMPONENTS; PROCESS-BASED APPROACH; REAL WORLD DATA; SIMULATED DATA; TRANSFORMED VARIABLES;

COMPUTER SIMULATION; DATA ENVELOPMENT ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 80255125983     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.09.080     Document Type: Article

References (24)

1. N. Alder, and J. Berechman Measuring airport quality from the airlines' viewpoint: An application of data envelopment analysis Transport Policy 8 2001 171 181 (Pubitemid 32836365)
2. N. Alder, and B. Golany Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe European Journal of Operational Research 132 2001 260 273 (Pubitemid 32385019)
3. N. Alder, and B. Golany Including principal component weights to improve discrimination in data envelopment analysis Journal of Operations Research Society of Japan 46 2002 66 73
4. A. Charnes, W.W. Cooper, and E. Rhodes Measuring the efficiency of decision making units European Journal of Operational Research 2 1978 429 444 (Pubitemid 9402758)
5. S. Chen, S.A. Bilings, and W. Luo Orthogonal least squares methods and their application to non-linear system identification International Journal of Control 50 1989 1873 1896 (Pubitemid 20636409)
6. C.S. Cinca, and C.M. Molinero Selecting DEA specifications and ranking units via PCA Journal of the Operational Research Society 55 2004 521 528
7. A.A. Foroughi, and M. Tamiz An effective total ranking model for a ranked voting system Omega 33 2005 491 496 (Pubitemid 40792484)
8. R.H. Green, J.R. Doyle, and W.D. Cook Preference voting and project ranking using DEA and cross-evaluation European Journal of Operational Research 90 1996 461 472 (Pubitemid 126369041)
9. A. Hashimoto A Ranked voting system using a DEA/AR exclusion model: a note European Journal of Operational Research 97 1997 600 604 (Pubitemid 127418523)
10. S.K. Jain, and A.D. Gunawardena Linear algebra: An interactive approach 2003 Thomson Learning Books/Cole, USA
11. L. Jenkins, and M. Anderson A multivariate statistical approach to reducing the number of variables in data envelopment analysis European Journal of Operational Research 147 2003 51 61
12. L. Liang, Y. Li, and S. Li Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA Expert Systems with Applications 36 2009 5895 5899
13. C. Lina, C. Chen, and C. Yena Improving the generalization performance of RBF neural networks via the use of a linear regression technique Expert Systems with Applications 36 2009 12049 12053
14. C.A.K. Lovell, and J.T. Pastor Radial DEA models without inputs or without outputs European Journal of Operational Research 118 1999 46 51
15. J.H. Min, and Y.-C. Lee A practical approach to credit scoring Expert Systems with Applications 35 2008 1762 1770
16. Y. Oussar, and G. Dreyfus Initialization by selection for wavelet network training Neurocomputing 34 2000 131 143 (Pubitemid 30657706)
17. J.T. Pastor Translation invariance in data envelopment analysis: A generalization Annals of Operations Research 66 1996 93 102
18. I.M. Premachandra A note on DEA vs principal component analysis: An improvement to Joe Zhu's approach European Journal of Operational Research 132 2001 553 560 (Pubitemid 32487097)
19. J. Ruggiero Performance evaluation when non-discretionary factors correlate with technical efficiency European Journal of Operational Research 159 2004 250 257
20. R. Shanmugam, and C. Johnson At a crossroad of data envelopment and principal component analysis Omega 35 2007 351 364 (Pubitemid 44426438)
21. H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar Ranking a random feature for variable and feature selection Journal of Machine Learning Research 3 2003 1399 1414
22. Wang, H. (1996). Elementary introduction to multiple correlations causing harm to principal component analysis. Journal of Beijing University of Aeronautics and Astronautics, 22 65-70. (in Chinese)
23. H. Wang, B. Yi, and M. Ye Variable selection based on principal basis analysis Journal of Beijing University of Aeronautics and Astronautics 34 2008 1288 1291 in Chinese
24. J. Zhu Data envelopment analysis vs principal component analysis: An illustrative study of economic performance of Chinese cities European Journal of Operational Research 111 1998 50 61 (Pubitemid 128401640)