Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems

  • Sascha HenzgenEmail author
  • Marc Strickert
  • Eyke Hüllermeier
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)


Evolving fuzzy systems are data-driven fuzzy (rule-based) systems supporting an incremental model adaptation in dynamically changing environments; typically, such models are learned on a continuous stream of data in an online manner. This paper advocates the use of visualization techniques in order to help a user gain insight into the process of model evolution. More specifically, rule chains are introduced as a novel visualization technique for the inspection of evolving Takagi-Sugeno-Kang (TSK) fuzzy systems. To show the usefulness of this techniques, we illustrate its application in the context of learning from data streams with temporal concept drift.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Sascha Henzgen
    • 1
    Email author
  • Marc Strickert
    • 1
  • Eyke Hüllermeier
    • 1
  1. 1.Department of Mathematics and Computer SciencePhilipps University MarburgMarburgGermany

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