International Journal of Engineering and Technology

Data Envelopment Analysis (DEA) is a method for measuring efficiencies of Decision Making Units (DMUs). While it has been widely used in many industrial and economic applications, for large DMUs with many inputs and outputs, DEA would require huge computer resources in terms of memory and CPU time. Several studies have attempted to overcome this problem for large datasets. However, the approaches used in the prior researches have some drawbacks which include uncontrolled convergence and non-generalization. Support Vector Regression (SVR) as a generalization from Support Vector Machine (SVM) is a powerful technique based on statistical learning theory for solving many prediction problems in the real-world applications. Hence, in this paper, a new combination of DEA and SVR, DEA-SVR, method is proposed and evaluated for large scale data sets. We evaluate and compare the proposed method using five large datasets used in earlier research. Experimental results demonstrates that the proposed method outperforms the recent most promising combined method of DEA and back-propagation neural networks, DEA-NNs, in terms of accuracy in efficiency estimation.