Hybrid radial basis function DEA and its applications to regression, segmentation and cluster analysis problems

Abstract

This paper uses a radial basis function (RBF) transformation of data envelopment analysis (DEA) data to perform RBF-DEA. It is shown that the RBF-DEA frontier identifies cases that have average efficiency scores in traditional DEA. The formal identification of average efficiency cases allows decision-makers to use these cases and related information for regression, segmentation and cluster analysis. Additionally, negative inputs and outputs can be used in RBF-DEA and unique ranking of fully efficient cases in traditional DEA can be achieved by further evaluating these fully efficient cases against the average RBF-DEA regression frontier. When compared to traditional cluster analysis, RBF-DEA cluster analysis offers unique advantages in that number of clusters do not need to be mentioned and cluster labels are identified by the RBF-DEA technique. Furthermore, unlike the traditional techniques, RBF-DEA cluster memberships are not sensitive to any initial random starting points.