A hybrid radial basis function and data envelopment analysis neural network for classification

Computers and Operations Research

Volumn 38, Issue 1, 2011, Pages 256-266

  Pendharkar, Parag C ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Data envelopment analysis; Linear programming; Neural networks; Radial basis functions

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA ENVELOPMENT ANALYSIS; FUNCTIONS; HEAT CONDUCTION; IMAGE SEGMENTATION; LINEAR PROGRAMMING; NEURAL NETWORKS;

BINARY CLASSIFICATION PROBLEMS; CLASSIFICATION FUNCTIONS; HIGH-DIMENSIONAL; LINEARLY SEPARABLE; MISCLASSIFICATION COSTS; PROBABILISTIC NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; SIMULATED DATASETS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 78049451756     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2010.05.001     Document Type: Conference Paper

References (30)

1. R.D. Banker, W.W. Cooper, L.M. Seiford, R.M. Thrall, and J. Zhu Returns to scale in different DEA models European Journal of Operational Research 154 2004 345 362
2. R.D. Banker, H.S. Chang, and W.W. Cooper Small sample properties of ML, COLS and DEA estimators of frontier models in the presence of heteroscedasticity European Journal of Operational Research 108 1998 140 148
3. A.N. Bojanic, S.B. Caudill, and J.M. Ford A Comment European Journal of Operational Research 136 2002 466 467
4. R.D. Banker, and R.M. Thrall Estimation of returns to scale using data envelopment analysis European Journal of Operational Research 62 1992 74 84
5. R.D. Banker, A. Charnes, and W.W. Cooper Some models for estimating technical and scale inefficiencies in DEA Management Science 20 9 1984 1078 1092
6. S. Bhattacharyya, and P.C. Pendharkar Inductive, evolutionary and neural techniques for discrimination: A comparative study Decision Sciences 29 4 1998 871 900
7. A. Charnes, W.W. Cooper, and E. Rhodes Measuring the efficiency of decision making units European Journal of Operational Research 2 6 1978 429 444
8. W.W. Cooper, L.M. Seiford, and K. Tone Data envelopment analysis: a comprehensive text with models, applications, references and DEA solver software 2000 Kluwer Academic Publishers Norwell, MA
9. T.M. Cover Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition IEEE Transactions on Electronic Computers EC-14 1965 326 334
10. S. Haykin Neural networks: a comprehensive foundation 1999 Prentice-Hall Englewood Cliffs, NJ
11. C.W. Hsu, and C.J. Lin A comparison of methods for multi-class support vector machines IEEE Transactions on Neural Networks 13 2 2002 415 425
12. G.R. Jahanshahloo, M. Soleimani-Damaneh, and M. Reshadi On pareto (dynamically) efficient paths International Journal of Computer Mathematics 83 8&9 2006 631 635
13. P.C. Pendharkar Exhaustive and heuristic search approaches for learning a software defect prediction model Engineering Applications of Artificial Intelligence 23 1 2010 34 40
14. P.C. Pendharkar A potential use of DEA for inverse classification problem Omega: An International Journal of Management Science 30 2002 243 248
15. P.C. Pendharkar An empirical study of design and testing of hybrid evolutionary-neural approach for classification Omega: An International Journal of Management Science 29 4 2001 361 374
16. D.E. Rumelhart, G.E. Hinton, and R.J. Williams Learning internal representations by error propagation D.E. Rumelhart, J.L. McClelland, Pparallel distributed processing: exploration in the microstructure of cognition, foundations VVol. 1 1986 MIT Press Cambridge, MA
17. R.J. Schalkoff Artificial neural networks 1997 McGraw-Hill NY
18. B. Scholkopf, K.K. Sung, C.J.C. Burges, F. Girosi, P. Niyogi, and T. Poggio Comparing support vector machine with gaussian kernels to radial basis function classifiers IEEE Transactions on Neural Networks 45 11 1997 2758 2765
19. L.M. Seiford, and J. Zhu An acceptance system decision rule with data envelopment analysis Computers and Operations Research 25 4 1998 329 332
20. D. Shi, D.S. Yeung, and J. Gao Sensitivity analysis applied to the construction of radial basis function networks Neural Networks 18 2005 951 957
21. M. Soleimani-Damaneh, and M. Reshadi A polynomial-time algorithm to estimate returns to scale in FDH models Computers & Operations Research 34 2007 2168 2176
22. M. Soleimani-Damaneh, G.R. Jahanshahloo, and M. Reshadi On the estimation of returns-to-scale in FDH models European Journal of Operational Research 174 2006 1055 1059
23. D. Specht Probabilistic neural networks Neural Networks 3 1990 109 118
24. T. Sueyoshi DEA-discriminant analysis: Methodological comparison among eight discriminant analysis approaches European Journal of Operational Research 169 2006 247 272
25. T. Sueyoshi, and K. Sekitani Returns to scale in dynamic DEA European Journal of Operational Research 161 2005 536 544
26. T. Sueyoshi Extended DEA-discriminant analysis European Journal of Operational Research 131 2001 324 351
27. T. Sueyoshi DEA-discriminant analysis in the view of goal programming European Journal of Operational Research 115 1999 564 582
28. M.D. Troutt, A. Rai, and A. Zhang The potential use of DEA for credit applicant acceptance systems Computers & Operations Research 23 4 1996 405 408
29. H. Tulkens On FDH efficiency analysis: some methodological issues and applications to retail banking, courts, and urban transit The Journal of Productivity Analysis 4 1993 183 210
30. I.H. Witten, and E. Frank Data mining: practical machine learning tools and techniques 2005 Morgan Kaufmann Publishers San Francisco, CA