Rule improvement through decision boundary detection using sensitivity analysis

  • AP Engelbrecht
  • HL Viktor
Artificial Neural Nets Simulation and Implementation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1607)


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.


Artificial Neural Network Decision Boundary Attribute Evaluation Rule Extraction Rule Accuracy 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • AP Engelbrecht
    • 1
  • HL Viktor
    • 2
  1. 1.Department of Computer ScienceUniversity of PretoriaPretoriaSouth africa
  2. 2.Department of InformaticsUniversity of PretoriaPretoriaSouth Africa

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