Edwin Lughofer,
"All-Pairs Evolving Fuzzy Classifiers for On-line Multi-Class Classification Problems"
: Proceedings of the EUSFLAT 2011 conference, Seite(n) 372-379, 2011
Original Titel:
All-Pairs Evolving Fuzzy Classifiers for On-line Multi-Class Classification Problems
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the EUSFLAT 2011 conference
Original Kurzfassung:
In this paper, we propose a novel design of evolving
fuzzy classifiers in case of multi-class classification
problems. Therefore, we exploit the concept
of all-pairs aka all-versus-all classification using binary
classifiers for each pair of classes, which has
some advantages over direct multi-class as well as
one-versus-rest classification variants. Regressionbased
as well as singleton class label fuzzy classifiers
are used as architectures for the binary classifiers,
which are evolved and incrementally trained based
on the concepts included in the FLEXFIS family
(a connection of eVQ and recursive fuzzily weighted
least squares). The classification phase considers
the preference levels of each pair of classes stored
in a preference relation matrix and uses a weighted
voting scheme of preference levels, including reliability
aspects. The advantage of the new evolving
fuzzy classifier concept over single model (using
direct multi-class classification concept) and multi
model (using one-versus-rest classification concept)
architectures will be underlined by empirical evaluations
and comparisons at the end of the paper based
on high-dimensional real-world multi-class classification
problems.
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