Super efficiency SBM-DEA and neural network for performance evaluation

Abstract

The traditional data envelopment analysis (DEA) method used for performance evaluation has inherent problems such as being easily affected by statistical noise in data. Furthermore, when new evaluation units are added, the performance of all the original units must be re-measured, which restricts the evaluation efficiency. In this study, machine learning algorithms were applied to make up for the shortcomings of the data envelopment analysis method. First, a super-efficiency SBM model was used to construct the relative effective frontier, and then machine learning algorithms were used to construct a regression model and establish the absolute effective frontier. After 15 machine learning algorithms were compared, BPNN demonstrated the best performance, and a SuperSBM-DEA-BPNN model was eventually established. The new model has the following advantages: First, compared with the traditional data envelopment analysis method, the absolute effective frontier displays better evaluation; second, compared with the data envelopment analysis and neural network fusion outlined in the previous literature, the new model can better overcome the problems associated with data envelopment analysis, thereby improving the fusion efficiency. Taking the innovation efficiency evaluation of China's regional rural commercial banks for instance, the new model is proven to be more applicable and offers more effective management tools to improve efficiency. On the whole, the new model not only provides a stable performance evaluation tool but also facilitates comparison, which has good application significance for organizations.