PROCEEDINGS IPMU '08
Classifiers based on granular structures from rough inclusions
Lech Polkowski and Piotr Artiemjew.
Granular computing initiated by
L.A.Zadeh aims at computing with
granules of knowledge i.e. with classes
of objects similar with respect to a
chosen representation of knowledge. It
is assumed that classes of sufficiently
similar objects behave satisfactorily
similarly in solutions of problems of
decision making, classification, fusion
of knowledge, approximate reasoning.
In this work, we support this assumption with a study of classifiers based
on granular structures. Granules of
knowledge are defined here by means
of rough inclusions as proposed by
Polkowski and from granules computed
in this way in a data set, granular
reflections are produced which are basis
for inducing classifiers.
We demonstrate a few basic rough inclusions and we show that classifiers
obtained from granulated according to
them data sets yield results better than
the standard exhaustive rough set classifier.
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