Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Sandra Benítez Peña, Peter Bogetoft, Dolores Romero Morales

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.
Original languageEnglish
Article number102068
JournalOmega: The International Journal of Management Science
Volume96
Number of pages11
ISSN0305-0483
DOIs
Publication statusPublished - Oct 2020

Bibliographical note

Published online: 30. May 2019

The research presented in the contribution was funded by the H2020 Marie Skłodowska-Curie Actions grant ‘Research and Innovation Staff Exchange Network of European Data Scientists' (#822214 – NeEDS).

Keywords

  • Benchmarking
  • Data envelopment analysis
  • Feature selection
  • Mixed Integer Linear Programming

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