Computational intelligence techniques: a study of scleroderma skin disease

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

@InProceedings{1274028,
  author =       "Julio J. Valdes and Alan J. Barton",
  title =        "Computational intelligence techniques: a study of
                 scleroderma skin disease",
  booktitle =    "Late breaking paper at Genetic and Evolutionary
                 Computation Conference {(GECCO'2007)}",
  year =         "2007",
  month =        "7-11 " # jul,
  editor =       "Peter A. N. Bosman",
  isbn13 =       "978-1-59593-698-1",
  pages =        "2580--2587",
  address =      "London, United Kingdom",
  keywords =     "genetic algorithms, genetic programming, differential
                 evolution, genomics, grid computing, hybrid
                 evolutionary-classical optimisation, Particle Swarm
                 Optimisation, rough sets, scleroderma disease,
                 similarity structure preservation, virtual reality,
                 visual data mining",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2580.pdf",
  DOI =          "doi:10.1145/1274000.1274028",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper presents an analysis of microarray gene
                 expression data from patients with and without
                 scleroderma skin disease using computational
                 intelligence and visual data mining techniques. Virtual
                 reality spaces are used for providing unsupervised
                 insight about the information content of the original
                 set of genes describing the objects. These spaces are
                 constructed by hybrid optimization algorithms based on
                 a combination of Differential Evolution (DE) and
                 Particle Swarm Optimization respectively, with
                 deterministic Fletcher-Reeves optimisation. A
                 distributed-pipelined data mining algorithm composed of
                 clustering and cross-validated rough sets analysis is
                 applied in order to find subsets of relevant attributes
                 with high classification capabilities. Finally, genetic
                 programming (GP) is applied in order to find explicit
                 analytic expressions for the characteristic functions
                 of the scleroderma and the normal classes. The virtual
                 reality spaces associated with the set of function
                 arguments (genes) are also computed. Several small
                 subsets of genes are discovered which are capable of
                 classifying the data with complete accuracy. They
                 represent genes potentially relevant to the
                 understanding of the scleroderma disease.",
  notes =        "Distributed on CD-ROM at GECCO-2007 ACM Order No.
                 910071",
}

Genetic Programming entries for Julio J Valdes Alan J Barton

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