Detecting and Reacting on Drifts and Shifts in On-Line Data Streams with Evolving Fuzzy Systems
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
IFSA/EUSFLAT conference 2009
Sprache des Tagungstitel:
Englisch
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.