Synthetic Data Generation for Data Envelopment Analysis
The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real dat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/10/146 |
| _version_ | 1797574173803937792 |
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| author | Andrey V. Lychev |
| author_facet | Andrey V. Lychev |
| author_sort | Andrey V. Lychev |
| collection | DOAJ |
| description | The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole. |
| first_indexed | 2024-03-10T21:20:11Z |
| format | Article |
| id | doaj.art-f5d11e5bf67d4edfb9811dfcb820b6a6 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T21:20:11Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-f5d11e5bf67d4edfb9811dfcb820b6a62023-11-19T16:11:23ZengMDPI AGData2306-57292023-09-0181014610.3390/data8100146Synthetic Data Generation for Data Envelopment AnalysisAndrey V. Lychev0College of Information Technologies and Computer Sciences, National University of Science and Technology “MISIS”, 4 Leninsky Ave., Bldg. 1, 119049 Moscow, RussiaThe paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole.https://www.mdpi.com/2306-5729/8/10/146synthetic data generationdata augmentationresearch datadata envelopment analysisweight restrictions |
| spellingShingle | Andrey V. Lychev Synthetic Data Generation for Data Envelopment Analysis Data synthetic data generation data augmentation research data data envelopment analysis weight restrictions |
| title | Synthetic Data Generation for Data Envelopment Analysis |
| title_full | Synthetic Data Generation for Data Envelopment Analysis |
| title_fullStr | Synthetic Data Generation for Data Envelopment Analysis |
| title_full_unstemmed | Synthetic Data Generation for Data Envelopment Analysis |
| title_short | Synthetic Data Generation for Data Envelopment Analysis |
| title_sort | synthetic data generation for data envelopment analysis |
| topic | synthetic data generation data augmentation research data data envelopment analysis weight restrictions |
| url | https://www.mdpi.com/2306-5729/8/10/146 |
| work_keys_str_mv | AT andreyvlychev syntheticdatagenerationfordataenvelopmentanalysis |