Increasing the Discriminatory Power of DEA Using Shannon’s Entropy
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 traditi...
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MDPI AG
2014-03-01
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Series: | Entropy |
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Online Access: | http://www.mdpi.com/1099-4300/16/3/1571 |
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author | Qiwei Xie Qianzhi Dai Yongjun Li An Jiang |
author_facet | Qiwei Xie Qianzhi Dai Yongjun Li An Jiang |
author_sort | Qiwei Xie |
collection | DOAJ |
description | 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. |
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format | Article |
id | doaj.art-0b13fd30b83446f1a231d96d4779299d |
institution | Directory Open Access Journal |
issn | 1099-4300 |
language | English |
last_indexed | 2024-04-11T13:18:46Z |
publishDate | 2014-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Entropy |
spelling | doaj.art-0b13fd30b83446f1a231d96d4779299d2022-12-22T04:22:18ZengMDPI AGEntropy1099-43002014-03-011631571158510.3390/e16031571e16031571Increasing the Discriminatory Power of DEA Using Shannon’s EntropyQiwei Xie0Qianzhi Dai1Yongjun Li2An Jiang3Department of Electronics and Information, Toyota Technological Institute, Nagoya 468-8511, JapanSchool of Business, University of Science and Technology of China, Hefei 230026, Anhui Province, ChinaSchool of Business, University of Science and Technology of China, Hefei 230026, Anhui Province, ChinaResearch Center for Eco-Environment Sciences, Chinese Academy of Sciences, Beijing 100085, ChinaIn 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.http://www.mdpi.com/1099-4300/16/3/1571data envelopment analysis (DEA)discrimination improvementShannon’s entropy |
spellingShingle | Qiwei Xie Qianzhi Dai Yongjun Li An Jiang Increasing the Discriminatory Power of DEA Using Shannon’s Entropy Entropy data envelopment analysis (DEA) discrimination improvement Shannon’s entropy |
title | Increasing the Discriminatory Power of DEA Using Shannon’s Entropy |
title_full | Increasing the Discriminatory Power of DEA Using Shannon’s Entropy |
title_fullStr | Increasing the Discriminatory Power of DEA Using Shannon’s Entropy |
title_full_unstemmed | Increasing the Discriminatory Power of DEA Using Shannon’s Entropy |
title_short | Increasing the Discriminatory Power of DEA Using Shannon’s Entropy |
title_sort | increasing the discriminatory power of dea using shannon s entropy |
topic | data envelopment analysis (DEA) discrimination improvement Shannon’s entropy |
url | http://www.mdpi.com/1099-4300/16/3/1571 |
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