Fitness Functions in Genetic Programming for Classification with Unbalanced Data

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

  author =       "Grant Patterson and Mengjie Zhang",
  title =        "Fitness Functions in Genetic Programming for
                 Classification with Unbalanced Data",
  booktitle =    "Proceedings of the 20th Australian Joint Conference on
                 Artificial Intelligence",
  year =         "2007",
  editor =       "Mehmet A. Orgun and John Thornton",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  pages =        "769--775",
  address =      "Gold Coast, Australia",
  month =        dec # " 2-6",
  bibsource =    "DBLP,",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-76926-2",
  DOI =          "doi:10.1007/978-3-540-76928-6_90",
  size =         "11 pages",
  abstract =     "This paper describes a genetic programming (GP)
                 approach to binary classification with class imbalance
                 problems. This approach is examined on two benchmark
                 and two synthetic data sets. The results show that when
                 using the overall classification accuracy as the
                 fitness function, the GP system is strongly biased
                 toward the majority class. Two new fitness functions
                 are developed to deal with the class imbalance problem.
                 The experimental results show that both of them
                 substantially improve the performance for the minority
                 class, and the performance for the majority and
                 minority classes is much more balanced.",
  notes =        "Pima",

Genetic Programming entries for Grant Patterson Mengjie Zhang