Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jason Moore",
  title =        "Cross Validation Consistency for the Assessment of
                 Genetic Programming Results in Microarray Studies",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
                 Evo{MUSART}, Evo{ROB}, Evo{STIM}",
  year =         "2003",
  editor =       "G{\"u}nther R. Raidl and Stefano Cagnoni and 
                 Juan Jes\'us Romero Cardalda and David W. Corne and 
                 Jens Gottlieb and Agn\`es Guillot and Emma Hart and 
                 Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and 
                 Martin Middendorf",
  volume =       "2611",
  series =       "LNCS",
  pages =        "99--106",
  address =      "University of Essex, UK",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, applications",
  isbn13 =       "978-3-540-00976-4",
  DOI =          "doi:10.1007/3-540-36605-9_10",
  abstract =     "DNA microarray technology has made it possible to
                 measure the expression levels of thousands of genes
                 simultaneously in a particular cell or tissue. The
                 challenge for computational biologists and
                 bioinformaticists will be to develop methods that are
                 able to identify subsets of gene expression variables
                 and features that classify cells and tissues into
                 meaningful biological and clinical groups. Genetic
                 programming (GP) has emerged as a machine learning tool
                 for variable and feature selection in microarray data
                 analysis. However, a limitation of GP is a lack of
                 cross validation strategies for the assessment of GP
                 results. This is partly due to the inherent complexity
                 of GP due to its stochastic properties. Here, we
                 introduce and review cross validation consistency (CVC)
                 as a new modeling strategy for use with GP. We review
                 the application of CVC to symbolic discriminant
                 analysis (SDA), a GP-based analytical strategy for
                 mining gene expression patterns in DNA microarray
  notes =        "EvoWorkshops2003",

Genetic Programming entries for Jason H Moore