Lifting the Curse of Dimensionality

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "W. P. Worzel and A. Almal and C. D. MacLean",
  title =        "Lifting the Curse of Dimensionality",
  booktitle =    "Genetic Programming Theory and Practice {IV}",
  year =         "2006",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =       "5",
  series =       "Genetic and Evolutionary Computation",
  pages =        "29--40",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-387-33375-4",
  DOI =          "doi:10.1007/978-0-387-49650-4_3",
  abstract =     "In certain problem domains the 'Curse of
                 Dimensionality' [Hastie et al., 2001] is well known.
                 Also known as the problem of 'High P and Low N' where
                 the number of parameters far exceeds the number of
                 samples to learn from, we describe our methods for
                 making the most of limited samples in producing
                 reasonably general classification rules from data with
                 a larger number of parameters. We discuss the
                 application of this approach in classifying
                 mesothelioma samples from baseline data according to
                 their time to recurrence. In this case there are over
                 12625 inputs for each sample but only 19 samples to
                 learn from. We reflect on the theoretical implications
                 of the behaviour of GP in these extreme cases and
                 speculate on the nature of generality.",
  notes =        "part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop",

Genetic Programming entries for William P Worzel Arpit A Almal Duncan MacLean