Learning with missing data using Genetic Programming

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

  author =       "Gerriet Backer",
  title =        "Learning with missing data using Genetic Programming",
  booktitle =    "The 1st Online Workshop on Soft Computing (WSC1)",
  year =         "1996",
  address =      "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
  month =        "19--30 " # aug,
  organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
                 and Systems (SOFT)",
  publisher =    "Nagoya University, Japan",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Missing data, Strongly Typed Genetic
                 Programming STGP",
  URL =          "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/d041.html",
  URL =          "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz",
  abstract =     "Learning with imprecise or missing data has been a
                 major challenge for machine learning. A new approach
                 using Strongly Typed Genetic Programming is proposed
                 here, which uses extra computations based on other
                 input data to approximate the missing values. It
                 eliminates the need for pre-processing and makes use of
                 correlations between the input data. The decision
                 process itself and the handling of unknown data can be
                 extracted from the resulting program for an analysis
                 afterwards. Comparing it to an alternative approach on
                 a simple example shows the usefulness of this
  size =         "5 pages",
  notes =        "Adds {"}unknown{"} data type to STGP. demo on iris
                 classification problem (see discussion on WSC1 pages)
                 email WSC1 organisers wsc@bioele.nuee.nagoya-u.ac.jp",

Genetic Programming entries for Gerriet Backer