Rule and Feature Extraction from Neural Networks

1 Rule and Feature Extraction from Neural Networks 

Neural networks are often viewed as black boxes. Despite the high level of predictive accuracy, one usually cannot understand why a particular outcome is predicted. Although this is generally true, especially for multilayer networks whose weights can not be easily interpreted, there are methods for analyzing trained networks and extracting rules or features. The issue can be framed as different set of questions. How does one extract rules from trained networks (13.2.1) Is it possible to measure the importance of inputs (13.2.2) How should input variables be selected (13.2.3) Another related question concerns the interpretation of network output How likely is the prediction to be correct (13.2.4)  

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