Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull
本文通过将随机森林方法应用于自由处置壳技术，提出了一种新的超效率测量方法，该方法对重采样稳健，能缓解维度诅咒问题，并引入输入变量重要性评估。
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.