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Full-Text Articles in Physical Sciences and Mathematics
Using Reconstructability Analysis To Select Input Variables For Artificial Neural Networks, Stephen Shervais, Martin Zwick
Using Reconstructability Analysis To Select Input Variables For Artificial Neural Networks, Stephen Shervais, Martin Zwick
Systems Science Faculty Publications and Presentations
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.
Ordering Genetic Algorithm Genomes With Reconstructability Analysis, Stephen Shervais, Martin Zwick
Ordering Genetic Algorithm Genomes With Reconstructability Analysis, Stephen Shervais, Martin Zwick
Systems Science Faculty Publications and Presentations
The building block hypothesis implies that genetic algorithm effectiveness is influenced by the relative location of epistatic genes on the chromosome. We find that this influence exists, but depends on the generation in which it is measured. Early in the search process it may be more effective to have epistatic genes widely separated. Late in the search process, effectiveness is improved when they are close together. The early search effect is weak but still statistically significant; the late search effect is much stronger and plainly visible. We demonstrate both effects with a set of simple problems, and show that infonnation-theoretic …