Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems
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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