Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Sandra Benítez Peña, Peter Bogetoft, Dolores Romero Morales

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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.
TidsskriftOmega: The International Journal of Management Science
Antal sider11
StatusUdgivet - okt. 2020

Bibliografisk note

Published online: 30. May 2019


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