María D. Guillén (Center of Operations Research, Miguel Hernandez University of Elche), Juan Aparicio (Center of Operations Research, Miguel Hernandez University of Elche; Valencian Graduate School and Research Network of Artificial Intelligence (valgrAI) and Miriam Esteve (Center of Operations Research, Miguel Hernandez University of Elche)
Abstract:
This paper aims to show how to calculate different efficiency measures using a technology estimator defined through the adaptation of the Gradient Tree Boosting algorithm. This adaptation shares some features with the standard nonparametric Free Disposal Hull (FDH) approach, but it overcomes data overfitting problems. Nevertheless, from a computational point of view, the new approach presents thousands of decision variables, making it difficult to solve. To tackle this problem, we also propose and check a heuristic approximation to the exact measures. To demonstrate the applicability of the proposed method, the exact and the heuristic approaches are compared through two empirical applications. The main contributions of this paper are as follows: we build a new bridge between machine learning techniques and technical efficiency measurement. In this framework, we show how to determine the output-oriented and input-oriented radial models, the Russell measure of output efficiency and the Russell measure of input efficiency, as well as the directional distance function and the Enhanced Russell Graph measure. We also prove that the new technique is better, in terms of bias and squared mean error, than the standard FDH technique. Furthermore, we show that the new approach may be seen as a possible remedy for solving the curse of dimensionality problem.