Efficiency analysis trees: A new methodology for estimating production frontiers through decision trees

Esteve, Miriam; Aparicio, Juan; Rabasa, A.; Rodríguez-Sala, Jesús Javier

doi:10.1016/j.eswa.2020.113783

Cited by 52 publications

(39 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The order in which the node is expanded is crucial because the final result depends on it. In the standard EAT algorithm [22], a node t is selected to be split when it is not a leaf node and it has few nodes in ( )…”

Section: A a Heuristic Algorithm Based On The Eat Methodsmentioning

confidence: 99%

“…Efficiency Analysis Trees (EAT) [22] is a nonparametric technique to estimate the current level of efficiency of a set of entities based on a tree structure. EAT generates a production frontier which satisfies fundamental postulates of microeconomics, such as free disposability, and shares some similarities with the Free Disposal Hull (FDH) [11] and Classification and Regression Trees (CART) [23].…”

Section: Efficiency Analysis Treesmentioning

confidence: 99%

“…However, none of the above methodologies for estimating production functions addresses the problem through standard machine learning techniques, as for example Regression Trees, despite being one of the hot topics in the literature on modern data analytics. One recent exception is [22], where Classification and Regression Trees (CART) by [23] were adapted for estimating production functions through step functions, there by competing against the well-known FDH technique. The new approach was named Efficiency Analysis Trees (EAT).…”

Section: Introductionmentioning

confidence: 99%

“…The new approach was named Efficiency Analysis Trees (EAT). Esteve et al [22] showed how EAT clearly outperforms FDH both in terms of mean squared error and bias. See also [24], [25] and [26] to read about the recent interest of the non-parametric community in bridging the gap between FDH and DEA and data science, machine learning and big data.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, through our approach, we will be able to decrease the computational burden of standard backtracking techniques applied to the decision tree methodology based on EAT, as we will show in our simulated experiences. As far as we are aware, our paper is the first one that contemplates computational aspects of the Efficiency Analysis Trees technique [22]. On the one hand, there are some previous papers devoted to efficiency measurement and related to computational features of the problem (see, for example, [31] and [32]).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Heuristic and Backtracking Algorithms for Improving the Performance of Efficiency Analysis Trees

et al. 2021

Self Cite

View full text Add to dashboard Cite

In the literature of Economics, Engineering and Operations Research, the estimation of production frontiers is a current hot topic. Many parametric and nonparametric methodologies have been introduced for estimating technical efficiency of a set of units (for example, firms) from the production frontier. However, few of these methodologies are based upon machine learning techniques, despite being a rising field of research. Recently, a bridge has been built between these literatures, machine learning and production theory, through a new technique proposed in Esteve et al (2020), called Efficiency Analysis Trees (EAT). The algorithm developed from EAT, based on the well-known Classification and Regression Trees (CART) machine learning technique, is a greedy technique that uses a particular heuristic for the selection of the next node to be split during the decision tree development process. Nevertheless, as we show in this paper, for different sample sizes and number of variables, the heuristic used by EAT is not capable of obtaining the tree with the minimum mean square error (MSE). For this reason, in this paper, a backtracking technique is implemented to improve the MSE obtained by the EAT algorithm. Additionally, a pair of new algorithms are introduced which combine the heuristic technique used by the standard EAT and the backtracking algorithm to enhance the reduction of the MSE, while decreasing the computation time. Our research is based on some simulated experiments. According to our computational results, the combination of the heuristic and the backtracking algorithm, in particular, that in which the tree growth starts with heuristics and ends with backtracking, has achieved an accuracy similar to that of backtracking and within a reasonable computational time. The contribution of the paper could be of special interest for industrial engineers interested in measuring efficiency and productivity of industrial processes in many sectors, such as energy, agri-food or service industries.

show abstract

Section: A a Heuristic Algorithm Based On The Eat Methodsmentioning

confidence: 99%

Section: Efficiency Analysis Treesmentioning

confidence: 99%