Evolving Coevolutionary Classifiers under Large Attribute Spaces

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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