Data Mining using Genetic Programming: The Implications of Parsimony on Generalization Error

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

@InProceedings{cavaretta:1999:DMGPTIPGE,
  author =       "Michael J. Cavaretta and Kumar Chellapilla",
  title =        "Data Mining using Genetic Programming: The
                 Implications of Parsimony on Generalization Error",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1330--1337",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 GP-variant, common dataset, data mining heuristic,
                 generalisation error, less complex models, program
                 complexity, training error, unseen data, computational
                 complexity, data mining, generalisation (artificial
                 intelligence)",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  DOI =          "doi:10.1109/CEC.1999.782602",
  size =         "8 pages",
  abstract =     "A common data mining heuristic is, 'when choosing
                 between models with the same training error, less
                 complex models should be preferred as they perform
                 better on unseen data'. This heuristic may not always
                 hold. In genetic programming a preference for less
                 complex models is implemented as: (i) placing a limit
                 on the size of the evolved program; (ii) penalising
                 more complex individuals, or both. The paper presents a
                 GP-variant with no limit on the complexity of the
                 evolved program that generates highly accurate models
                 on a common dataset",
  notes =        "statlog

                 CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

Genetic Programming entries for Michael J Cavaretta Kumar Chellapilla

Citations