# Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

arXiv: https://arxiv.org/abs/2002.12362 · HTML: https://mlsanity.com/paper/2002.12362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Endogenous Aggregation of Multiple Data Envelopment Analysis Scores for Large Data Sets](https://mlsanity.com/paper/2510.20052/) (2025)

## Abstract (arXiv)

This paper proposes an integrative approach to feature (input and output) selection in Data Envelopment Analysis (DEA). The DEA model is enriched with zero-one decision variables modelling the selection of features, yielding a Mixed Integer Linear Programming formulation. This single-model approach can handle different objective functions as well as constraints to incorporate desirable properties from the real-world application. Our approach is illustrated on the benchmarking of electricity Distribution System Operators (DSOs). The numerical results highlight the advantages of our single-model approach provide to the user, in terms of making the choice of the number of features, as well as modeling their costs and their nature.