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.
|Journal||Omega: The International Journal of Management Science|
|Number of pages||11|
|Publication status||Published - Oct 2020|
Bibliographical notePublished online: 30. May 2019
The research presented in the contribution was funded by the H2020 Marie Skłodowska-Curie Actions grant ‘Research and Innovation Staff Exchange Network of European Data Scientists' (#822214 – NeEDS).
- Data envelopment analysis
- Feature selection
- Mixed Integer Linear Programming