The impact of decision-making units features on efficiency by integration of data envelopment analysis, artificial neural network, fuzzy C-means and analysis of variance

International Journal of Operational Research

Volumn 7, Issue 3, 2010, Pages 387-411

  Azadeh, Ali ^a   Javanmardi, Leili ^a   Saberi, Morteza ^b  

^a University College of Engineering   (Iran)

^b TAFRESH UNIVERSITY   (Iran)

Author keywords

Analysis of variance; ANN; ANOVA; Artificial neural network; Banks; Data envelopment analysis; Data mining; DEA; Efficiency; FCM; Fuzzy C means

Indexed keywords

EID: 77951456474     PISSN: 17457645     EISSN: 17457653     Source Type: Journal    
DOI: 10.1504/IJOR.2010.032113     Document Type: Article

References (77)

1. Akhavein, J. D., Berger, A. N. and Humphrey, D. B. (1997) 'The effects of megamergers on efficiency and prices: evidence from a bank profit function', Review of Industrial Organizations, Vol. 12, pp. 95-139.
2. Arbib, M. A. (2003) The Handbook of Brain Theory and Neural Networks (2nd ed.). Cambridge, MA: MIT Press.
3. Athanassopoulos, A. D. and Curram, S. P. (1996) 'A comparison of data envelopment analysis and artificial neural networks as tool for assessing the efficiency of decision making units', Journal of the Operational Research Society, Vol. 47, pp. 1000-1016.
4. Athanassopoulos, A. D. (1997) 'Service quality and operating efficiency synergies for management control in the provision of financial services: evidence from Greek bank branches', European Journal of Operational Research, Vol. 98, pp. 300-313.
5. Athanassopoulos, A. D. and Giokas, D. (2000) 'The use of data envelopment analysis in banking institutions: evidence from the commercial bank of Greece', Interfaces, Vol. 30, pp. 81-95.
6. Avkiran, N. K. (2006) 'Developing foreign bank efficiency models for DEA grounded in finance theory', Socio-Economic Planning Sciences, Vol. 40, pp. 275-296.
7. Azadeh, A. and Tarverdian, S. (2007) 'Integration of genetic algorithm, computer simulation and design of experiment for forecasting electrical consumption', Energy Policy, Vol. 35, pp. 5229-5241.
8. Azadeh, A., Ghaderi, S. F. and Sohrabkhani, S. (2007) 'Forecasting electrical consumption by integration of neural network, time series and ANOVA', Applied Mathematics and Computation, Vol. 186, pp. 1753-1761.
9. Azadeh, A., Ghaderi, S. F., Anvari, M., Saberi, M. and Izadbakhsh, H. (2007a) 'An integrated artificial neural network and fuzzy clustering algorithm for performance assessment of decision making units', Applied Mathematics and Computation, Vol. 187, pp. 584-599.
10. Azadeh, A., Ghaderi, S. F., Anvari, M. and Saberi, M. (2007b) 'Performance assessment of electric power generations using an adaptive neural network algorithm', Energy Policy, Vol. 35, pp. 3155-3166.
11. Azadeh, A., Saberi, M., Ghaderi, S. F., Gitiforouz, A. and Ebrahimipour, V. (2008) 'Improved estimation of electricity demand function by integration of fuzzy system and data mining aapproach', Energy Conversion and Management, Vol. 49, pp. 2165-2177.
12. Aznarte, J. L., Sanchez, J. M. B., Lugilde, D. N., Fernandez, C. D. L., Guardia, C. D. and Sanchez, F. A. (2007) 'Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models', Expert Systems with Applications, Vol. 32, pp. 1218-1225.
13. Bauer, P. W. (1990) 'Recent developments in the econometric estimation of frontiers', Journal of Econometrics, Vol. 46, pp. 39-56.
14. Becerra-Fernandez, I., Zanakis, S. H. and Walczak, S. (2002) 'Knowledge discovery techniques for predicting country investment risk', Computers and Industrial Engineering, Vol. 43, pp. 787-800.
15. Bergendahl, G. and Lindblom, T. (2008) 'Evaluating the performance of Swedish savings banks according to service efficiency', European Journal of Operational Research, Vol. 185, pp. 1663-1673.
16. Berger, A. N. and Humphrey, D. B. (1991) 'The dominance of inefficiencies over scale and product mix economies in banking', Journal of Monetary Economics, Vol. 28, pp. 117-148.
17. Berger, A. N. and Humphrey, D. B. (1997) 'Efficiency of financial institutions: international survey and directions for future research', European Journal of Operational Research, Vol. 98, pp. 175-212.
18. Berger, A. N., Hancock, D. and Humphrey, D. B. (1993) 'Bank efficiency derived from the profit function', Journal of Banking and Finance, Vol. 17, pp. 317-348.
19. Beynon, M. J. and Peel, M. J. (2001) 'Variable precision rough set theory and data discretisation: an application to corporate failure prediction', Omega, Vol. 29, pp. 561-576.
20. Camanho, A. S. and Dyson, R. G. (2005) 'Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments', European Journal of Operational Research, Vol. 161, pp. 432-446.
21. Charnes, A., Cooper, W. W., Lewin, A. Y. and Seiford, L. M. (1994) Data Envelopment Analysis: Theory, Methodology and Applications. Boston, MA: Kluwer Academic Publishers.
22. Chen, X., Skully, M. and Brown, K. (2005) 'Banking efficiency in China: application of DEA to pre-and post-deregulation eras: 1993-2000', China Economic Review, Vol. 16, pp. 229-245.
23. Chou, H. C., Cheng, C. H. and Chang, J. R. (2007) 'Extracting drug utilization knowledge using selforganizing map and rough set theory', Expert Systems with Applications, Vol. 33, pp. 499-508.
24. Clark, J. A. (1996) 'Economic cost, scale efficiency, and competitive viability in banking', Journal of Money, Banking and Credit, Vol. 28, pp. 342-364.
25. Costa, A. and Markellos, R. N. (1997) 'Evaluating public transport efficiency with neural network models', Transportation Research C, Vol. 5, pp. 301-312.
26. Deyoung, R. (1997) 'A diagnostic test for the distribution-free efficiency estimator: an example using US commercial bank data', European Journal of Operational Research, Vol. 98, pp. 243-249.
27. Deyoung, R. (1998) 'Management quality and X-inefficiency in national banks', Journal of Financial Services Research, Vol. 13, pp. 5-22.
28. Drake, L., Hall, M. J. B. and Simper, R. (2006) 'The impact of macroeconomic and regulatory factors on bank efficiency: a non-parametric analysis of Hong Kong's banking system', Journal of Banking and Finance, Vol. 30, pp. 1443-1466.
29. Gareta, R., Romeo, L. M. and Gil, A. (2006) 'Forecasting of electricity prices with neural networks', Energy Conversion and Management, Vol. 47, pp. 1770-1778.
30. Golany, B. and Storbeck, J. E. (1999) 'A data envelopment analysis of the operation efficiency of bank branches', Interfaces, Vol. 29, pp. 14-26.
31. Hassanien, A. (2007) 'Fuzzy rough sets hybrid scheme for breast cancer detection', Image and Vision Computing, Vol. 25, pp. 172-183.
32. Ho, C-T. B. (2007) 'Performance measurement using data envelopment analysis and financial statement analysis', Int. J. Operational Research, Vol. 2, pp. 26-38.
33. Ho, C-T. B. and Wu, D. D. (2007) 'Online banking performance evaluation using data envelopment analysis and principal component analysis', Computers and Operations Research.
34. Hao, J. Hunter, W. C. and Yang, W. K. (2001) 'Deregulation and efficiency: the case of private Korean banks', Journal of Economics and Business, Vol. 53, pp. 237-254, March-June.
35. Jain, A. and Kumar, A. M. (2007) 'Hybrid neural network models for hydrologic time series forecasting', Applied Soft Computing, Vol. 7, pp. 585-592.
36. Kao, C. and Liu, S. T. (2004) 'Predicting bank performance with financial forecasts: a case of Taiwan commercial banks', Journal of Banking and Finance, Vol. 28, pp. 2353-2368.
37. Kao, C. and Liu, S-T. (in press) 'Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks', European Journal of Operational Research.
38. Kaparakis, E. I., Miller, S. M. and Noulas, A. G. (1994) 'Short-run cost inefficiency of commercial banks: a flexible stochastic frontier approach', Journal of Money, Credit and Banking, Vol. 26, pp. 875-894.
39. Karunasinghea, D. S. K. and Liong, S. Y. (2006) 'Chaotic time series prediction with a global model artificial neural network', Journal of Hydrology, Vol. 323, pp. 92-105.
40. Komorowski, J., Polkowski, L. and Skowron, A. (1998) Rough Sets: A Tutorial, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU).
41. Kowalczyk, W. and Slisser, F. (1997) 'Modeling customer retention with rough data models', Paper presented in the Proceedings of the First European Symposium PKDD'97, Trondheim, Norway, pp. 4-13.
42. Kusiak, A. and Tseng, B. T. L. (2000) 'Data mining in engineering design: a case study', Paper presented in the Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 1, pp. 206-211.
43. Lili, W. and Zhi, P. (2001) 'Fuzziness in rough sets based on general relations', Journal of Ningxia University (Natural Science), Vol. 22, pp. 352-355.
44. Liu, M., Chen, D., Wu, C. and Li, H. (2006) 'Reduction method based on a new fuzzy rough set in fuzzy information system and its applications to scheduling problems', Computers and Mathematics with Applications, Vol. 51, pp. 1571-1584.
45. Montgomery, D. C. (1999) Design and Analysis of Experiments. New York, NY: Jhon Wiley & Sons.
46. Mostafa, M. M. (2009) 'Modeling the efficiency of top Arab banks: a DEA-neural network approach', Expert Systems with Applications, Vol. 36, pp. 309-320, January.
47. Maria Conceiçó, A. S. P. and Emmanuel, T. (2007) 'Comparative efficiency analysis of Portuguese bank branches', European Journal of Operational Research, Vol. 177, pp. 1275-1288
48. Nayak, P. C., Sudheer, K. P., Rangan, D. M. and Ramasastri, K. S. (2004) 'A neuro-fuzzy computing technique for modeling hydrological time series', Journal of Hydrology, Vol. 291, pp. 52-66.
49. Niskaa, H., Hiltunen, T., Karppinen, A., Ruuskanen, J. and Kolehmainena, M. (2004) 'Evolving the neural network model for forecasting air pollution time series', Engineering Applications of Artificial Intelligence, Vol. 17, pp. 159-167.
50. Oliveira, A. L. I. and Meira, S. R. L. (2006) 'Detecting novelties in time series through neural networks forecasting with robust confidence intervals', Operational Research, Vol. 70, pp. 79-92.
51. Pawlak, Z. (1991) Rough Sets: Theoretical Aspects of Reasoning about Data. Boston, MA: Kluwer.
52. Pendharkar, P. C. and Rodger, J. A. (2003) 'Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption', Decision Support Systems, Vol. 36, pp. 117-136.
53. Portela, M. C. A. S. and Thanassoulis, E. (2007) 'Comparative efficiency analysis of Portuguese bank branches', European Journal of Operational Research, Vol. 177, pp. 1275-1288.
54. Ravi Kumar, P. and Ravi, V. (2007) 'Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review', European Journal of Operational Research, Vol. 180, pp. 1-28, July.
55. Ruhe, G. (1996) 'Qualitative analysis of software engineering data using rough sets', Paper presented in the Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery, Tokyo, Japan, pp. 292-299.
56. Sahoo, B. K. and Tone, K. (in press) 'Decomposing capacity utilization in data envelopment analysis: an application to banks in India', European Journal of Operational Research.
57. Sanchis, A., Segovia, M. J., Gil, J. A., Heras, A. and Vilar, J. L. (2007) 'Rough sets and the role of the monetary policy in financial stability (macroeconomic problem) and the prediction of insolvency in insurance sector (microeconomic problem) ', European Journal of Operational Research, Vol. 181, pp. 1554-1573.
58. Santin, D. and Valino, A. (2000) 'Artificial neural networks for measuring technical efficiency in schools with a two-level model: an alternative approach', II Oviedo Workshop on Efficiency and Productivity.
59. Santin, D., Delgado, F. J. and Valino, A. (2004) 'The measurement of technical efficiency: a neural network approach', Applied Economics, Vol. 36, pp. 627-635.
60. Sherman, H. D. and Gold, F. (1985) 'Bank branch operating efficiency: evaluation with data envelopment analysis', Journal of Banking and Finance, Vol. 9, pp. 316-297.
61. Shyng, J. Y., Wang, F. K., Tzeng, G. H. and Wu, K. S. (2007) 'Rough set theory in analyzing the attributes of combination values for the insurance market', Expert Systems with Applications, Vol. 32, pp. 56-64.
62. Sikder, I. U. and Gangopadhyay, A. (2007) 'Managing uncertainty in location services using rough set and evidence theory', Expert Systems with Applications, Vol. 32, pp. 386-396.
63. Soteriou, A. and Zenios, S. A. (1999) 'Operations, quality and profitability in the provision of banking services', Management Science, Vol. 45, pp. 1221-1238.
64. Stefanowski, J. (1998) 'Rough set based rule induction techniques for classification problems', Paper presented in the Proceddings of the 6th European Congress on Intelligent Techniques and Soft Computing, Vol. 1, Aachen September 7-10, pp. 109-113.
65. Su, C. T. and Hsu, J. H. (2006) 'Precision parameter in the variable precision rough sets model: an application', Omega, Vol. 34, pp. 149-157.
66. Tortosa-Ausina, E., Grifell-Tatjé, E., Armero, C. and Conesa, D. (2008) 'Sensitivity analysis of efficiency and malmquist productivity indices: an application to Spanish savings banks', European Journal of Operational Research, Vol. 184, pp. 1062-1084.
67. Triantaphyllou, E., Liao, T. W. and Iyengar, S. S. (2002) 'A focused issue on data mining and knowledge discovery in industrial engineering', Computers and Industrial Engineering, Vol. 43, pp. 657-659.
68. Tseng, T. L. and Huang, C. C. (2007) 'Rough set-based approach to feature selection in customer relationship management', Omega, Vol. 35, pp. 365-383.
69. Tseng, F. M., Yu, H. C. and Tzeng, G. H. (2002) 'Combining neural network model with seasonal time series ARIMA model', Technological Forecasting and Social Change, Vol. 69, pp. 71-87.
70. Tsumoto, S. (1997) 'Extraction of experts' decision process from clinical databases using rough set model', Paper presented in the Proceedings of the First European Symposium PKDD '97, Trondheim, Norway, pp. 58-67.
71. Tulkens, H. (1993) 'On FDH efficiency analysis: some methodological issues and applications to retail banking, courts and urban transit', Journal of Productivity Analysis, Vol. 4, pp. 183-210.
72. Wenxiu, Z. and Weizhi, W. (2000) 'An introduction and survey for the studies of rough set theory', Fuzzy System and Mathematics, Vol. 14, pp. 1-12.
73. Wojnarski, M. (2003) LTF-C: Architecture, Training Algorithm and Application of New Neural Classifier, Fundamenta Informaticae XX. Amsterdam, The Netherlands: IOS Press, pp. 1-17.
74. Wu, D., Yang, Z. and Liang, L. (2006) 'Efficiency analysis of cross-region bank branches using fuzzy data envelopment analysis', Applied Mathematics and Computation, Vol. 181, pp. 271-281.
75. Xia, W. and Wu, Z. (2007) 'Supplier selection with multiple criteria in volume discount environments', Omega, Vol. 35, pp. 494-504, October. (Pubitemid 44835388)
76. Zhai, L. Y., Khoo, L. P. and Fok, S. C. (2002) 'Feature extraction using rough set theory and genetic algorithms - an application for the simplification of product quality evaluation', Computers and Industrial Engineering, Vol. 43, pp. 661-676.
77. Ziarko (1993) 'Variable precision rough set model', Journal of Computer and System Sciences, Vol. 46, pp. 39-59.