Genetic Programming with a Genetic Algorithm for Feature Construction and Selection

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

  author =       "Matthew G. Smith and Larry Bull",
  title =        "Genetic Programming with a Genetic Algorithm for
                 Feature Construction and Selection",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2005",
  volume =       "6",
  number =       "3",
  pages =        "265--281",
  month =        sep,
  note =         "Published online: 17 August 2005",
  keywords =     "genetic algorithms, genetic programming, feature
                 construction, feature selection, classification,
                 machine learning",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-005-2988-7",
  size =         "17 pages",
  abstract =     "The use of machine learning techniques to
                 automatically analyse data for information is becoming
                 increasingly widespread. In this paper we primarily
                 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 such
                 features are the most predictive. Using ten well-known
                 datasets we show that our approach, in comparison to
                 C4.5 alone, provides marked improvement in a number of
                 cases. We then examine its use with other well-known
                 machine learning techniques.",
  notes =        "UCI datasets. Mentions ADFs but these are separate
                 trees within the same chromosome, they do not call each
                 other, nor are they called. k-nearest neighbours, naive
                 Bayes. GAP. C4.8=J48. Wrapper. Random tree
                 initialisation. Missing values lead to zero. p2568
                 inversion. p269 Ensures no missing data attributes at
                 the start of the second stage.",

Genetic Programming entries for Matthew G Smith Larry Bull