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Combining Data Envelopment Analysis and Machine Learning

Author

Listed:
  • Nadia M. Guerrero

    (Center of Operations Research (CIO), Miguel Hernandez University of Elche (UMH), 03202 Elche, Spain)

  • Juan Aparicio

    (Center of Operations Research (CIO), Miguel Hernandez University of Elche (UMH), 03202 Elche, Spain)

  • Daniel Valero-Carreras

    (Center of Operations Research (CIO), Miguel Hernandez University of Elche (UMH), 03202 Elche, Spain)

Abstract

Data Envelopment Analysis (DEA) is one of the most used non-parametric techniques for technical efficiency assessment. DEA is exclusively concerned about the minimization of the empirical error, satisfying, at the same time, some shape constraints (convexity and free disposability). Unfortunately, by construction, DEA is a descriptive methodology that is not concerned about preventing overfitting. In this paper, we introduce a new methodology that allows for estimating polyhedral technologies following the Structural Risk Minimization (SRM) principle. This technique is called Data Envelopment Analysis-based Machines (DEAM). Given that the new method controls the generalization error of the model, the corresponding estimate of the technology does not suffer from overfitting. Moreover, the notion of ε -insensitivity is also introduced, generating a new and more robust definition of technical efficiency. Additionally, we show that DEAM can be seen as a machine learning-type extension of DEA, satisfying the same microeconomic postulates except for minimal extrapolation. Finally, the performance of DEAM is evaluated through simulations. We conclude that the frontier estimator derived from DEAM is better than that associated with DEA. The bias and mean squared error obtained for DEAM are smaller in all the scenarios analyzed, regardless of the number of variables and DMUs.

Suggested Citation

  • Nadia M. Guerrero & Juan Aparicio & Daniel Valero-Carreras, 2022. "Combining Data Envelopment Analysis and Machine Learning," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:909-:d:769571
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    References listed on IDEAS

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    Cited by:

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    2. Georgios Tsaples & Jason Papathanasiou & Andreas C. Georgiou, 2022. "An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU," Mathematics, MDPI, vol. 10(13), pages 1-27, June.

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