Rule improvement through decision boundary detection using sensitivity analysis
Rule extraction from artificial neural networks (ANN) provides a mechanism to interpret the knowledge embedded in the numerical weights. Classification problems with continuous-valued parameters create difficulties in determining boundary conditions for these parameters. This paper presents an approach to locate such boundaries using sensitivity analysis. Inclusion of this decision boundary detection approach in a rule extraction algorithm resulted in significant improvements in rule accuracies.
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