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Entropy 2014, 16(3), 1571-1585; doi:10.3390/e16031571

Increasing the Discriminatory Power of DEA Using Shannon’s Entropy

1
Department of Electronics and Information, Toyota Technological Institute, Nagoya 468-8511, Japan
2
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3
School of Business, University of Science and Technology of China, Hefei 230026, Anhui Province, China
4
Research Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing 100085, China
*
Authors to whom correspondence should be addressed.
Received: 8 November 2013 / Revised: 14 January 2014 / Accepted: 5 March 2014 / Published: 20 March 2014
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Abstract

In many data envelopment analysis (DEA) applications, the analyst always confronts the difficulty that the selected data set is not suitable to apply traditional DEA models for their poor discrimination. This paper presents an approach using Shannon’s entropy to improve the discrimination of traditional DEA models. In this approach, DEA efficiencies are first calculated for all possible variable subsets and analyzed using Shannon’s entropy theory to calculate the degree of the importance of each subset in the performance measurement, then we combine the obtained efficiencies and the degrees of importance to generate a comprehensive efficiency score (CES), which can observably improve the discrimination of traditional DEA models. Finally, the proposed approach has been applied to some data sets from the prior DEA literature. View Full-Text
Keywords: data envelopment analysis (DEA); discrimination improvement; Shannon’s entropy data envelopment analysis (DEA); discrimination improvement; Shannon’s entropy
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Xie, Q.; Dai, Q.; Li, Y.; Jiang, A. Increasing the Discriminatory Power of DEA Using Shannon’s Entropy. Entropy 2014, 16, 1571-1585.

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