Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

S. Benítez Peña, D. Romero Morales, P. Bogetoft

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.

Keywords: Benchmarking, Data Envelopment Analysis, Feature Selection, Mixed Integer Linear Programming

RM-2 Ramiro Melendreras Award
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