A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis(DEA)is a linear programming methodology for measuring the efficiency of Decision Making Units(DMUs)to improve organizational performance in the private and public sectors.However,if a new DMU needs to be known its efficiency score,the DEA analysis would have to be re-conducted,especially nowadays,datasets from many fields have been growing rapidly in the real world,which will need a huge amount of computation.Following the previous studies,this paper aims to establish a linkage between the DEA method and machine learning(ML)algorithms,and proposes an alternative way that combines DEA with ML(ML-DEA)algorithms to measure and predict the DEA efficiency of DMUs.Four ML-DEA algorithms are discussed,namely DEA-CCR model combined with back-propagation neural network(BPNN-DEA),with genetic algorithm(GA)integrated with back-propagation neural network(GANN-DEA),with support vector machines(SVM-DEA),and with improved support vector machines(ISVM-DEA),respectively.To illustrate the applicability of above models,the performance of Chinese manufacturing listed companies in 2016 is measured,predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model.The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%,and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA,BPNN-DEA,ISVM-DEA,and SVM-DEA.