Jimi Shanahan, James Baldwin, Trevor Martin
proceeding of CEC 99 (Congress on Evolutionary Computation), Washington D. C.
Cartesian granule features are derived features that are formed over the cross product of words that
linguistically partition the universes of the constituent input features. Both classification and prediction
problems can be modelled quite naturally in terms of Cartesian granule features incorporated into rule-based
models. The induction of Cartesian granule feature models involves discovering which input features should be
combined to form Cartesian granule features in order to model a domain effectively; an exponential search
problem. In this paper we present the G_DACG (Genetic Discovery of Additive Cartesian Granule feature
models) constructive induction algorithm as a means of automatically identifying additive Cartesian granule
feature models from example data. G_DACG combines the powerful optimisation capabilities of genetic
programming with a rather novel and cheap fitness function which relies on the semantic separation of learnt
concepts expressed in terms of Cartesian granule fuzzy sets. G_DACG is illustrated on a variety of artificial
and real world classification problems.
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