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Evolving Systems

, Volume 5, Issue 3, pp 175–191 | Cite as

Visualization of evolving fuzzy rule-based systems

  • Sascha Henzgen
  • Marc Strickert
  • Eyke HüllermeierEmail author
Original Paper

Abstract

Evolving fuzzy systems are data-driven fuzzy (rule-based) systems supporting an incremental mode of 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, dynamic parallel coordinates, vertical parallel coordinates and rule chains are introduced as novel visualization techniques for the inspection of evolving Takagi–Sugeno–Kang (TSK) fuzzy systems. These techniques are realized in the software tool fuzzy inference system visualizer, the architecture and functionality of which are presented in this work. To show the usefulness of the proposed techniques, we illustrate their application in the context of learning from data streams with temporal concept drift.

Keywords

Visualization Evolving fuzzy systems TSK systems Model evolution Data streams Concept drift 

Notes

Acknowledgments

This project is supported by the German Research Foundation (DFG). The authors also acknowledge financial support by the LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg. Moreover, they like to thank Edwin Lughofer for providing the FLEXFIS system.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sascha Henzgen
    • 1
  • Marc Strickert
    • 2
  • Eyke Hüllermeier
    • 1
    Email author
  1. 1.Department of Computer ScienceUniversity of PaderbornPaderbornGermany
  2. 2.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany

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