Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

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.
Comment: This research has been financed in part by the EC H2020 MSCA RISE NeEDS Project (Grant agreement ID: 822214); the EU COST Action MI-NET (TD 1409); and research projects MTM2015-65915R, Spain, FQM-329, Junta de Andaluc\'ia, these two with EU ERF funds. This support is gratefully acknowledged