Classifying with uncertain data envelopment analysis
Classifications organize entities into categories that identify similarities within a category and discern dissimilarities among categories, and they powerfully classify information in support of analysis. We propose a new classification scheme premised on the reality of imperfect data. Our computat...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | EURO Journal on Computational Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192440624000078 |
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| _version_ | 1846123440331816960 |
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| author | Casey Garner Allen Holder |
| author_facet | Casey Garner Allen Holder |
| author_sort | Casey Garner |
| collection | DOAJ |
| description | Classifications organize entities into categories that identify similarities within a category and discern dissimilarities among categories, and they powerfully classify information in support of analysis. We propose a new classification scheme premised on the reality of imperfect data. Our computational model uses uncertain data envelopment analysis to define a classification's proximity to equitable efficiency, which is an aggregate measure of intra-similarity within a classification's categories. Our classification process has two overriding computational challenges, those being a loss of convexity and a combinatorially explosive search space. We overcome the first challenge by establishing lower and upper bounds on the proximity value, and then by searching this range with a first-order algorithm. We address the second challenge by adapting the p-median problem to initiate our exploration, and by then employing an iterative neighborhood search to finalize a classification. We conclude by classifying the thirty stocks in the Dow Jones Industrial average into performant tiers, by classifying prostate treatments into clinically effectual categories, and dividing airlines into peer groups. |
| format | Article |
| id | doaj-art-c4e1d0d06c374588b10cc9a90d0e5ba3 |
| institution | Kabale University |
| issn | 2192-4406 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EURO Journal on Computational Optimization |
| spelling | doaj-art-c4e1d0d06c374588b10cc9a90d0e5ba32024-12-14T06:30:53ZengElsevierEURO Journal on Computational Optimization2192-44062024-01-0112100090Classifying with uncertain data envelopment analysisCasey Garner0Allen Holder1Dept. of Mathematics, University of Minnesota, Minneapolis, MN, USADept. of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA; Corresponding author.Classifications organize entities into categories that identify similarities within a category and discern dissimilarities among categories, and they powerfully classify information in support of analysis. We propose a new classification scheme premised on the reality of imperfect data. Our computational model uses uncertain data envelopment analysis to define a classification's proximity to equitable efficiency, which is an aggregate measure of intra-similarity within a classification's categories. Our classification process has two overriding computational challenges, those being a loss of convexity and a combinatorially explosive search space. We overcome the first challenge by establishing lower and upper bounds on the proximity value, and then by searching this range with a first-order algorithm. We address the second challenge by adapting the p-median problem to initiate our exploration, and by then employing an iterative neighborhood search to finalize a classification. We conclude by classifying the thirty stocks in the Dow Jones Industrial average into performant tiers, by classifying prostate treatments into clinically effectual categories, and dividing airlines into peer groups.http://www.sciencedirect.com/science/article/pii/S2192440624000078Data envelopment analysisRobust optimizationClassificationClustering |
| spellingShingle | Casey Garner Allen Holder Classifying with uncertain data envelopment analysis EURO Journal on Computational Optimization Data envelopment analysis Robust optimization Classification Clustering |
| title | Classifying with uncertain data envelopment analysis |
| title_full | Classifying with uncertain data envelopment analysis |
| title_fullStr | Classifying with uncertain data envelopment analysis |
| title_full_unstemmed | Classifying with uncertain data envelopment analysis |
| title_short | Classifying with uncertain data envelopment analysis |
| title_sort | classifying with uncertain data envelopment analysis |
| topic | Data envelopment analysis Robust optimization Classification Clustering |
| url | http://www.sciencedirect.com/science/article/pii/S2192440624000078 |
| work_keys_str_mv | AT caseygarner classifyingwithuncertaindataenvelopmentanalysis AT allenholder classifyingwithuncertaindataenvelopmentanalysis |