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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
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| Series: | EURO Journal on Computational Optimization |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192440624000078 |
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| Summary: | 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. |
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| ISSN: | 2192-4406 |