Context (archaeology)

Random forest

Engineering

Biology

Statistics

Marine engineering

Resampling

Curse of dimensionality

Archaeology

Economics

Computer science

Mathematical optimization

Paleontology

Physics

Operations research

Mathematics

Hull

Optics

Estimator

Geography

Industrial engineering

Econometrics

Environmental science

Operations management

Artificial intelligence

Efficiency

Machine learning

Adaptation (eye)

European Journal of Operational Research

In the technical efficiency evaluation area, it may happen that many observations obtain a similar relative technical efficiency status, making it difficult to discriminate between them. The determination of super-efficiency has been a way of solving this problem by providing a method to differentiate between the performance of observations. Despite the existence of some approaches dealing with the notion of super-efficiency in the literature, there have been few attempts to address this problem from the standpoint of machine learning techniques. In this paper, we fill this gap by adapting Random Forest to determine super-efficiency in the context of the Free Disposal Hull (FDH) technique. The new super-efficiency approach is robust to resampling on inputs and data. Additionally, we show how the new approach could be a possible solution for dealing with the curse of dimensionality problem; typically associated with FDH. Furthermore, exploiting the adaptation of Random Forest, a new method for assessing the importance of input variables is introduced. Finally, the advantages of the proposed approach are illustrated through a real example.

Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull