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Granular self-organizing map (grSOM) for structure identification.

Vassilis G Kaburlasos1, S E Papadakis

  • 1Department of Industrial Informatics, Division of Computing Systems, Technological Educational Institution of Kavala, GR 65404 Kavala, Greece. vgkabs@teikav.edu.gr

Neural Networks : the Official Journal of the International Neural Network Society
|September 27, 2005
PubMed
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This study introduces a granular Self-Organizing Map (SOM) for fuzzy system modeling, enhancing structure identification. The greedy granular SOM improves classification accuracy and generates interpretable fuzzy rules from data.

Area of Science:

  • Computational Intelligence
  • Machine Learning
  • Fuzzy Systems

Background:

  • Kohonen's Self-Organizing Map (KSOM) is a powerful tool for data analysis.
  • Linguistic system modeling often requires handling fuzzy and interval data.
  • Existing methods may struggle with ambiguity and extracting descriptive knowledge.

Purpose of the Study:

  • To extend KSOM for structure identification in linguistic (fuzzy) system modeling.
  • To introduce a granular SOM (grSOM) for inducing fuzzy interval numbers (FINs) from data.
  • To develop a greedy grSOM variant for improved classification and rule extraction.

Main Methods:

  • The granular SOM (grSOM) model processes linguistic (fuzzy) input data and intervals.
  • A novel metric distance d(K)(.,.) between FINs, tunable by a mass function m(x), is used for learning.

Related Experiment Videos

  • A genetic algorithm (GA) introduces tunable nonlinearities during training for the greedy grSOM.

    • Main Results:

    • The greedy grSOM demonstrated practical effectiveness in three benchmark classification problems.
    • Statistical evidence indicates improved classification performance compared to related work.
    • The greedy grSOM successfully induced descriptive decision-making knowledge in the form of fuzzy rules.

    Conclusions:

    • The proposed grSOM, particularly the greedy variant, offers a valuable extension for fuzzy system modeling.
    • This approach effectively handles ambiguity in linguistic data.
    • The method enhances classification performance and provides interpretable fuzzy rules.