Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

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.

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 Andalucía, these two with EU ERF funds. This support is gratefully acknowledged