GAP: Constructing and Selecting Features with Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Matthew G. Smith and Larry Bull",
  title =        "GAP: Constructing and Selecting Features with
                 Evolutionary Computation",
  booktitle =    "Evolutionary Computing in Data Mining",
  publisher =    "Springer",
  year =         "2004",
  editor =       "Ashish Ghosh and Lakhmi C. Jain",
  volume =       "163",
  series =       "Studies in Fuzziness and Soft Computing",
  chapter =      "3",
  pages =        "41--56",
  keywords =     "genetic algorithms, genetic programming, ADFs",
  ISBN =         "3-540-22370-3",
  URL =          ",11855,5-175-22-33980376-0,00.html",
  DOI =          "doi:10.1007/3-540-32358-9_3",
  abstract =     "The use of machine learning techniques to
                 automatically analyze data for information is becoming
                 increasingly widespread. In this chapter we examine the
                 use of Genetic Programming and a Genetic Algorithm to
                 pre-process data before it is classified using the C4.5
                 decision tree learning algorithm. Genetic Programming
                 is used to construct new features from those available
                 in the data, a potentially significant process for data
                 mining since it gives consideration to hidden
                 relationships between features. A Genetic Algorithm is
                 used to determine which set of features is the most
                 predictive. Using ten well-known data sets we show that
                 our approach, in comparison to C4.5 alone, provides
                 marked improvement in a number of cases.",
  notes =        "GA+GP system used to preprocess ten UCI datasets
                 constructing and selecting new features before using
  size =         "16 pages",

Genetic Programming entries for Matthew G Smith Larry Bull