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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)

Abstract

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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References

  1. 1.
    Alonso, J.M., Cordón, O., Quirin, A., Magdalena, L.: Analyzing interpretability of fuzzy rule-based systems by means of fuzzy inference-grams. In: World Congress on Soft Computing (2011)Google Scholar
  2. 2.
    Gabriel, T.R., Thiel, K., Berthold, M.R.: Rule visualization based on multi-dimensional scaling. In: 2006 IEEE International Conference on Fuzzy Systems, pp. 66–71. IEEE (2006)Google Scholar
  3. 3.
    Gama, J.: A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence 1(1), 45–55 (2012)CrossRefGoogle Scholar
  4. 4.
    Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering The Information Age - Solving Problems with Visual Analytics. In: Eurographics (2010)Google Scholar
  5. 5.
    Lughofer, E.: FLEXFIS: A robust incremental learning approach for evolving Takagi–Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems 16(6), 1393–1410 (2008)CrossRefGoogle Scholar
  6. 6.
    Lughofer, E., Hüllermeier, E.: On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure. In: Galichet, S., Montero, J., Mauris, G. (eds.) Proc. Eusflat–2011, 7th Int. Conf. of the European Soc. for Fuzzy Logic and Technology, pp. 380–387 (2011)Google Scholar
  7. 7.
    Lughofer, E., Bouchot, J.-L., Shaker, A.: On-line elimination of local redundancies in evolving fuzzy systems. Evolving Systems 2(3), 165–187 (2011)CrossRefGoogle Scholar
  8. 8.
    Peters, G., Bunte, K., Strickert, M., Biehl, M., Villmann, T.: Visualization of processes in self-learning systems. In: Tenth Annual International Conference on Privacy, Security and Trust (TSOS), pp. 244–249 (2012)Google Scholar
  9. 9.
    Rehm, F., Klawonn, F., Kruse, R.: Rule classification visualization of high-dimensional data. In: Proc. of the 11th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems, IPMU 2006 (2006)Google Scholar
  10. 10.
    Sayed-Mouchaweh, M., Lughofer, E.: Learning in non-stationary environments. Springer (2012)Google Scholar

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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