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Model generation by domain refinement and rule reduction.

T Sudkamp1, A Knapp, J Knapp

  • 1Dept. of Comput. Sci., Wright State Univ., Dayton, OH, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces a novel two-stage method to enhance fuzzy model interpretability by increasing rule granularity. The approach refines partitions and uses a greedy merging algorithm, reducing rules while maintaining precision.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Fuzzy Systems

Background:

  • Fuzzy model interpretability depends on rule base construction.
  • Heuristically derived models offer high granularity, while algorithmic models yield many low-granularity rules.
  • Existing methods struggle to balance granularity and precision.

Purpose of the Study:

  • To present a novel method for increasing fuzzy model rule granularity.
  • To satisfy a prescribed precision bound on training data.
  • To reduce the number of rules in fuzzy models.

Main Methods:

  • A two-stage process involving iterative partition refinement and a greedy merging algorithm.
  • Antecedents derived from decomposable partitions; consequents generated using proximity techniques.

Related Experiment Videos

  • Multi-dimensional antecedents in rules enhance representational capabilities.

    • Main Results:

    • The algorithm successfully increases rule granularity while adhering to precision bounds.
    • Demonstrated reduction in the number of rules in fuzzy models.
    • Effective for both precise and imprecise training data.

    Conclusions:

    • The proposed method enhances fuzzy model interpretability and efficiency.
    • It combines benefits of clustering and proximity methods for rule generation.
    • Offers a practical solution for developing more understandable and concise fuzzy systems.