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Radical pruning: a method to construct skeleton radial basis function networks.

M F Augusteijn1, K A Shaw

  • 1University of Colorado, Colorado Springs 80907, USA.

International Journal of Neural Systems
|August 12, 2000
PubMed
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This study introduces an efficient pruning method for radial basis function networks (RBFNs). The novel approach significantly reduces network weights, enhancing rule extraction capabilities with quadratic complexity.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Radial basis function networks (RBFNs) are effective for rule extraction due to locally tuned units.
  • Pruning RBFNs is crucial to eliminate redundant weights for practical rule extraction.
  • Exhaustive pruning is computationally infeasible for large networks.

Purpose of the Study:

  • To develop an efficient pruning algorithm for radial basis function networks.
  • To reduce the number of weights in RBFNs significantly while maintaining performance.
  • To improve the computational complexity of the pruning process.

Main Methods:

  • Utilized multiple pruning techniques.
  • Implemented a smart ordering strategy for pruning candidates.

Related Experiment Videos

  • Analyzed algorithm complexity in relation to the number of network weights.

    • Main Results:

    • Successfully reduced the number of weights in RBFNs to a small fraction of the original.
    • Achieved quadratic complexity for the pruning algorithm, a significant improvement over exponential complexity.
    • Demonstrated pruning performance on benchmark problems from the UCI machine learning database.

    Conclusions:

    • The proposed multiple pruning method with smart ordering is effective for RBFN weight reduction.
    • This approach enhances the feasibility of rule extraction from pruned RBFNs.
    • The method offers a computationally efficient solution for RBFN pruning.