Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach Articles uri icon

publication date

  • October 2020

start page

  • 1

end page

  • 11


  • 102068


  • 96

International Standard Serial Number (ISSN)

  • 0305-0483

Electronic International Standard Serial Number (EISSN)

  • 1873-5274


  • 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.


  • Statistics


  • benchmarking; data envelopment analysis; feature selection; mixed integer linear programming