Cascaded GP Models for Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Peter Lichodzijewski and Nur Zincir-Heywood and 
                 Malcolm Heywood",
  title =        "Cascaded GP Models for Data Mining",
  pages =        "2258--2264",
  booktitle =    "Proceedings of the 2004 IEEE Congress on Evolutionary
  year =         "2004",
  publisher =    "IEEE Press",
  month =        "20-23 " # jun,
  address =      "Portland, Oregon",
  ISBN =         "0-7803-8515-2",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2004.1331178",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The Cascade Architecture for incremental learning is
                 demonstrated within the context of Genetic Programming.
                 Such a scheme provides the basis for building steadily
                 more complex models until a desired degree of accuracy
                 is reached. The architecture is demonstrated for
                 several data mining datasets. Efficient training on
                 standard computing platforms is retained through the
                 use of the RSS-DSS algorithm for stochastically
                 sampling datasets in proportion to exemplar
                 'difficulty' and 'age'. Finally, the ensuing empirical
                 study provides the basis for recommending the utility
                 of sum square cost functions in the datasets
  notes =        "CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",

Genetic Programming entries for Peter Lichodzijewski Nur Zincir-Heywood Malcolm Heywood