keywords = "genetic algorithms, genetic programming, learning,
Cartesian granule fuzzy sets, G-DACG, Genetic Discovery
of Additive Cartesian Granule feature models , additive
Cartesian granule feature models, constituent input
features, constructive induction algorithm, exponential
search problem, fitness function, fuzzy Cartesian
granule feature models, linguistic partitioning,
optimisation capabilities, prediction problems, real
world classification problems, rule based models,
semantic separation, computational linguistics, fuzzy
set theory, learning by example, pattern
classification",
DOI = "doi:10.1109/CEC.1999.781929",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
abstract = "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 model involves discovering which input features
should be combined to form Cartesian granule features
in order to model a domain effectively; an exponential
search problem. 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",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.