Ammar Shaker, Edwin Lughofer,
"Resolving Global and Local Drifts in Data Stream Regression using Evolving Rule-Based Models"
: Proceedings of the IEEE SSCI 2013 Conference, Serie IEEE SSCI 2013 Conference, IEEE, Seite(n) 9-16, 2013
Original Titel:
Resolving Global and Local Drifts in Data Stream Regression using Evolving Rule-Based Models
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the IEEE SSCI 2013 Conference
Original Kurzfassung:
In this paper, we present new concepts for dealing
with drifts in data streams during the run of on-line modeling
processes for regression problems in the context of evolving fuzzy
systems. Opposed to the nominal case based on conventional
life-long learning, drifts are requiring a specific treatment for
the modeling phase, as they refer to changes in the underlying
data distribution or target concepts, which makes older learned
concepts obsolete. Our approach comes with three new stages for
an appropriate drift handling: 1.) drifts are not only detected, but
also quantified with a new extended version of the Page-Hinkley
test, which overcomes some instabilities during downtrends of
the indicator; 2.) based on the current intensity quantification
of the drift, the necessary degree of forgetting (weak to strong)
is extracted (adaptive forgetting); 3.) the latter is achieved by
two variants, a.) a single forgetting factor value, accounting for
global drifts, and b.) a forgetting factor vector with different
entries for separate regions of the feature space, accounting for
local drifts. Forgetting factors are integrated into the learning
scheme of both, the antecedent and consequent parts of the
evolving fuzzy systems. The new approach will be evaluated on
high-dimensional data streams, where the results will show that
1.) our adaptive forgetting strategy outperforms the usage of
fixed forgetting factors throughout the learning process and 2.)
forgetting in local regions may improve forgetting in global ones
when drifts appear locally.