# Classifying with Uncertain Data Envelopment Analysis

arXiv: https://arxiv.org/abs/2209.01052 · HTML: https://mlsanity.com/paper/2209.01052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Data-Driven Robust Optimization](https://mlsanity.com/paper/1401.0212/) (2013)
- [Distributionally Robust Optimization: A Review](https://mlsanity.com/paper/1908.05659/) (2019)
- [A Practical Guide to Robust Optimization](https://mlsanity.com/paper/1501.02634/) (2015)
- [Cross-benchmarking for performance evaluation: looking across best practices of different peer groups using DEA](https://mlsanity.com/paper/1912.01514/) (2019)
- [Uncertain Data Envelopment Analysis: Box Uncertainty](https://mlsanity.com/paper/2007.06972/) (2020)

## Abstract (arXiv)

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 by establishing lower and upper bounds on the proximity value, and then by searching this range with a first-order algorithm. We overcome the second 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 and by classifying prostate treatments into clinically effectual categories.