Edwin Lughofer, Mahardhika Pratama, Igor Skrjanc,
"Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation"
, in IEEE Transactions on Fuzzy Systems, Vol. 26, Nummer 4, IEEE Press, Seite(n) 1854-1865, 2018, ISSN: 1941-0034
Original Titel:
Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation
Sprache des Titels:
Englisch
Original Kurzfassung:
Gradual drifts in data streams are usually hard
to detect and often do not necessarily trigger the evolution
of new fuzzy rules during model adaptation steps in order to
represent the new, drifted data distribution(s) appropriately in the fuzzy model. Over time, they thus lead to oversized rules with untypically large local errors (typically also worsening the global model error), as representing joint local data distributions before and after a drift happened likewise. We therefore propose an incremental rule splitting concept for generalized fuzzy rules in order to autonomously compensate these negative effects of gradual drifts. Our splitting condition is based 1.) on the local error of rules measured in terms of a weighted contribution to the whole model error and 2.) on the size of the rules measured in terms of the volume of the associated clusters. We use the
concept of statistical process control in order to omit an extra threshold parameter in our splitting condition. The splitting technique relies on the eigen-decomposition of the rule covariance matrix to adequately manipulate the largest eigenvector and eigenvalues in order to retrieve the new centers and contours of the two split rules. Furthermore, we guarantee sufficient
flexibility in adapting the shapes and consequents of the split rules to the new drifted situation in the stream by integrating a specific dynamic and smooth forgetting concept of older samples, which formed the original (non-split) rules. Robustness against outliers is guaranteed by the realization of a two-layer model building process, where one layer represents the cluster partition and the other layer the rule partition: only clusters becoming significant over time are accepted as rules in the fuzzy model.
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