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Visualization of evolving fuzzy rule-based systems

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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.

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Notes

  1. It would arguably be more correct to solve a linear assignment problem Burkard and Dell’Amico (2009). However, since the computation needs to be repeated at every time step, this is computationally too expensive.

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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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Authors and Affiliations

  1. Department of Computer Science, University of Paderborn, Paderborn, Germany

    Sascha Henzgen & Eyke Hüllermeier

  2. Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany

    Marc Strickert

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  1. Sascha Henzgen
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Correspondence to Eyke Hüllermeier.

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Henzgen, S., Strickert, M. & Hüllermeier, E. Visualization of evolving fuzzy rule-based systems. Evolving Systems 5, 175–191 (2014). https://doi.org/10.1007/s12530-014-9110-4

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