Journal
COMPUTERS & OPERATIONS RESEARCH
Volume 38, Issue 1, Pages 256-266Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2010.05.001
Keywords
Data envelopment analysis; Radial basis functions; Neural networks; Linear programming
Ask authors/readers for more resources
We propose a hybrid radial basis function network-data envelopment analysis (RBFN-DEA) neural network for classification problems. The procedure uses the radial basis function to map low dimensional input data from input space 91 to a high dimensional R(+). feature space where DEA can be used to learn the classification function. Using simulated datasets for a non-linearly separable binary classification problem, we illustrate how the RBFN-DEA neural network can be used to solve it. We also show how asymmetric misclassification costs can be incorporated in the hybrid RBFN-DEA model. Our preliminary experiments comparing the RBFN-DEA with feed forward and probabilistic neural networks show that the RBFN-DEA fares very well. (C) 2010 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available