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A combined data envelopment analysis and support vector regression for efficiency evaluation of large decision making units

Farahmand, Mohammadreza and Desa, Mohammad Ishak and Nilashi, Mehrbakhsh (2014) A combined data envelopment analysis and support vector regression for efficiency evaluation of large decision making units. International Journal of Engineering and Technology, 6 (5). pp. 2310-2321. ISSN 2319-8613

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Official URL: http://www.enggjournals.com/ijet/docs/IJET14-06-05...

Abstract

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.

Item Type:Article
Uncontrolled Keywords:data envelopment analysis, decision making units, neural networks
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:51444
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 03:53
Last Modified:28 Jan 2019 04:30

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