Evolving Coevolutionary Classifiers under Large Attribute Spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Doucette:2009:GPTP,
-
author = "John Doucette and Peter Lichodzijewski and
Malcolm Heywood",
-
title = "Evolving Coevolutionary Classifiers under Large
Attribute Spaces",
-
booktitle = "Genetic Programming Theory and Practice {VII}",
-
year = "2009",
-
editor = "Rick L. Riolo and Una-May O'Reilly and
Trent McConaghy",
-
series = "Genetic and Evolutionary Computation",
-
address = "Ann Arbor",
-
month = "14-16 " # may,
-
publisher = "Springer",
-
chapter = "3",
-
pages = "37--54",
-
keywords = "genetic algorithms, genetic programming, Problem
Decomposition, Bid-based Cooperative Behaviors,
Symbiotic Coevolution, Subspace Classifier, Large
Attribute Spaces",
-
DOI = "doi:10.1007/978-1-4419-1626-6_3",
-
isbn13 = "978-1-4419-1653-2",
-
abstract = "Model-building under the supervised learning domain
potentially face a dual learning problem of identifying
both the parameters of the model and the subset of
(domain) attributes necessary to support the model,
thus using an embedded as opposed to wrapper or filter
based design. Genetic Programming (GP) has always
addressed this dual problem, however, further implicit
assumptions are made which potentially increase the
complexity of the resulting solutions. In this work we
are specifically interested in the case of
classification under very large attribute spaces. As
such it might be expected that multiple independent/
overlapping attribute subspaces support the mapping to
class labels; whereas GP approaches to classification
generally assume a single binary classifier per class,
forcing the model to provide a solution in terms of a
single attribute subspace and single mapping to class
labels. Supporting the more general goal is considered
as a requirement for identifying a 'team' of
classifiers with non-overlapping classifier behaviours,
in which each classifier responds to different subsets
of exemplars. Moreover, the subsets of attributes
associated with each team member might use a unique
'subspace' of attributes. This work investigates the
utility of coevolutionary model building for the case
of classification problems with attribute vectors
consisting of 650 to 100,000 dimensions. The resulting
team based coevolutionary evolutionary method-Symbiotic
Bid-based (SBB) GP-is compared to alternative embedded
classifier approaches of C4.5 and Maximum Entropy
Classification (MaxEnt). SSB solutions demonstrate up
to an order of magnitude lower attribute count relative
to C4.5 and up to two orders of magnitude lower
attribute count than MaxEnt while retaining comparable
or better classification performance. Moreover,
relative to the attribute count of individual models
participating within a team, no more than six
attributes are ever used; adding a further level of
simplicity to the resulting solutions.",
-
notes = "part of \cite{Riolo:2009:GPTP}",
- }
Genetic Programming entries for
John Doucette
Peter Lichodzijewski
Malcolm Heywood
Citations