Edwin Lughofer, Plamen Angelov,
"Detecting and Reacting on Drifts and Shifts in Online Data Streams with Evolving Fuzzy Systems"
: Proceedings of the IFSA/EUSFLAT 2009 conference, Seite(n) 931-937, 2009
Original Titel:
Detecting and Reacting on Drifts and Shifts in Online Data Streams with Evolving Fuzzy Systems
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the IFSA/EUSFLAT 2009 conference
Original Kurzfassung:
In this paper, we present new approaches to
handle drift and shift in on-line data streams using evolving
fuzzy systems (EFS), which are characterized by the fact that
their structure is not fixed and not pre-determined. When
dealing with drifts and shifts in data streams one needs to take
into account two major issues: a) automatic detection of, and
b) automatic reaction to this. To address the first problem we
propose an approach based on the concepts of age and utility
of fuzzy rules/clusters. The second problem itself is composed
of two sub-problems concerning the influence of the drifts and
shifts on: 1) the antecedent parts (fuzzy set and rule structure)
and 2) the consequent parts (parameters) of the fuzzy models.
To address the latter sub-problem we propose an approach
that introduces a gradual forgetting strategy in the local learning
process. To address the former sub-problem we introduce
two alternative methods: one that is based on the evolving
density-based clustering, eClustering (used in eTS); and one
that is based on the automatic adaptation of the learning rate
of the evolving vector quantization approach (eVQ) (used in
FLEXFIS). The paper is concluded with an empirical evaluation of the impact of the proposed approaches in (on-line) real-world data sets where drifts and shifts occur.