Using reconstructability analysis to select input variables for artificial neural networks
Stephen Shervais, Martin Zwick · 2004
We demonstrate the use of reconstructability analysis to reduce the number of input variables for a neural network. Using the heart disease dataset we reduce the number of independent variables from 13 to two, while providing results that are statistically indistinguishable from those of NNs using the full variable set. We also demonstrate that rule lookup tables obtained directly from the data for the RA models are almost as effective as NNs trained on model variables.