abstract = "A novel approach to the classification of large and
unbalanced multi-class data sets is presented where the
widely acknowledged issues of scalability, solution
transparency, and problem decomposition are addressed
simultaneously within the context of the Genetic
Programming (GP) paradigm. A cooperative coevolutionary
training environment that employs multi-objective
evaluation provides the basis for problem decomposition
and reduced solution complexity, while scalability is
achieved through a Pareto competitive coevolutionary
framework, allowing the system to be applied to large
data sets (tens or hundreds of thousands of exemplars)
without recourse to hardware-specific speedups.
Moreover, a key departure from the canonical GP
approach to classification is used in which the output
of GP is expressed in terms of a non-binary, local
membership function (e.g. a Gaussian), where it is no
longer necessary for an expression to represent an
entire class. Decomposition is then achieved through
reformulating the classification problem as one of
cluster consistency, where an appropriate subset of the
training patterns can be associated with each
individual such that problems are solved by several
specialist classifiers rather than by a single super
individual.",