Evolutionary Linear Discriminant Analysis for Multiclass Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4009

  author =       "Michael F. Korns",
  title =        "Evolutionary Linear Discriminant Analysis for
                 Multiclass Classification Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "233--234",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3075964",
  DOI =          "doi:10.1145/3067695.3075964",
  acmid =        "3075964",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, linear
                 discriminant analysis, symbolic regression and
  abstract =     "This paper implements Linear Discriminant Analysis
                 (LDA) together with genetic programming symbolic
                 classification for financial multiclass classification
                 problems. A very brief description of the matrix theory
                 of LDA is included. The implementation details in an
                 industrial strength multiclass classification system
                 are discussed. The algorithm is tested using
                 statistically correct, out of sample training and
                 testing. The algorithm's behaviour is demonstrated on
                 real world problems previously published as UCI test
                 suites and financial real world problems.",
  month =        "15-19 " # jul,
  notes =        "Also known as \cite{Korns:2017:ELD:3067695.3075964}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Michael Korns