A Comparative Study of CCR-(ε-SVR) and CCR-(ν-SVR) Models for Efficiency Prediction of Large Decision Making Units
In this paper, we develop CCR-(ε-SVR) and CCR-(ν-SVR) models based on modified parameters for efficiency prediction of large DMUs to improve the accuracy and reduce the computation time using three normalization functions. CCR-(ε-SVR) and CCR-(ν-SVR) are evaluated using large datasets over the three normalization functions. The experimental results of comparisons between CCR-(ε-SVR) and CCR-(ν-SVR) demonstrate that the proposed models can significantly improve the accuracy and reduce the computation time in predicting the efficiency of large DMUs.
Abe, S. (2010). Support vector machines for pattern classification. Springer.
Ali, A. I. (1993). Streamlined computation for data envelopment analysis. European journal of operational research, 64(1), 61-67.
Ali, A. I. (1994). Computational aspects of DEA (pp. 63-88). Springer Netherlands.
Ali, A. I., & Seiford, L. M. (1993). Computational accuracy and infinitesimals in data envelopment analysis. Infor, 31(4), 290-297.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis.Management science, 30(9), 1078-1092.
Barr, R. S., & Durchholz, M. L. (1997). Parallel and hierarchical decomposition approaches for solving large-scale data envelopment analysis models. Annals of Operations Research, 73, 339-372.
Barr, R. S., Killgo, K. A., Siems, T. F., & Zimmel, S. (2002). Evaluating the productive efficiency and performance of US commercial banks. Managerial Finance, 28(8), 3-25.
Bengio, Y., & Grandvalet, Y. (2004). No unbiased estimator of the variance of k-fold cross-validation. The Journal of Machine Learning Research, 5, 1089-1105.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), 27.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of econometrics, 30(1), 91-107.
Chen, C. M., & van Dalen, J. (2010). Measuring dynamic efficiency: Theories and an integrated methodology. European Journal of Operational Research, 203(3), 749-760.
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver Software. Second editions. Springer, ISBN, 387452818, 490.
Dulá, J. H. (2008). A computational study of DEA with massive data sets.Computers & Operations Research, 35(4), 1191-1203.
Emrouznejad, A., & Shale, E. (2009). A combined neural network and DEA for measuring efficiency of large scale datasets. Computers & Industrial Engineering, 56(1), 249-254.
Farahmand, M., Desa, M. I., & Nilashi, M. (2014). A Combined Data Envelopment Analysis and Support Vector Regression for Efficiency Evaluation of Large Decision Making Units. International Journal of Engineering and Technology (IJET). pp. 2310-2321.
Färe, R., & Knox Lovell, C. A. (1978). Measuring the technical efficiency of production. Journal of Economic theory, 19(1), 150-162.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 253-290.
Graf, A. B., & Borer, S. (2001). Normalization in support vector machines. Pattern Recognition (pp. 277-282). Springer Berlin Heidelberg.
Koopmans, T. C. (1951). Analysis of production as an efficient combination of activities. Activity analysis of production and allocation, 13, 33-37.
Milani, A. S., Shanian, A., Madoliat, R., & Nemes, J. A. (2005). The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection. Structural and multidisciplinary optimization, 29(4), 312-318.
Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research, 115(3), 596-607.
Samoilenko, S., & Osei-Bryson, K. M. (2010). Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operational Research, 206(2), 479-487.
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: an introduction (Vol. 104). Sage Publications.
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