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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Henzgen, S., Strickert, M., Hüllermeier, E. (2013). Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_27
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DOI: https://doi.org/10.1007/978-3-319-00969-8_27
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00968-1
Online ISBN: 978-3-319-00969-8
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