Combining machine learning techniques with NDEA methodology: the use of R.F. and A.N.N

Claudio Pinto

MPRA Paper from University Library of Munich, Germany

Abstract: The objective of the present work is to combine NDEA approach with machine learning techniques and neural networks. At this end we exploit the models proposed in Pinto, 2024. The integration process involves the application of a machine learning technique upstream of the resolution of NDEA models and the application of an artificial neural network downstream the resolution of a NDEA models. In particular here we propose the application of a Random Forest algorithm in regression models to adjust data on: 1) input and output, 2) resource allocation preferences among sub-processes, 3) cost budgets, revenue targets and profit targets, from the influence of internal and external factors in order to improve the calculation of optimal weights. Downstream of the resolution of NDEA models, the use of several artificial neural network models is to prosed to optimise the calculation of the economic quantities of interest derived from optimal NDEA solutions. The approach enhances the discrimination power and robustness of optimal NDEA weights as well as the robustness of the calculation of formulas of the economic quatities.

Keywords: Network Data Envelopment Analisys; Random Forest Regression; Artificial Neural Network; external factors (search for similar items in EconPapers)
JEL-codes: C45 C53 C61 L20 (search for similar items in EconPapers)
Date: 2025-09-07
New Economics Papers: this item is included in nep-big and nep-cmp
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