Evaluating the Efficiency of DMUs with PCA and an Application in Real Data Set of Iranian Banks

This paper proposes a method for evaluating the efficiency of decision making units (DMUs) by using principal component analysis. This efficiency deals with undesirable outputs and simultaneously reduces the dimensionality of data set. First, we change the undesirable outputs to be desirable by reversing. Then we do PCA on the ratios of a single desirable output to a single input. In order to reduce the dimensionality of data set, the required principal components are selected out of the generated ones according to the lowest eigenvalues. Finally these chosen principal components are treated as virtual data set into data envelopment analysis (DEA). Then the utility of proposed approach is applied to real data set of some branches of an Iranian bank.