A Comparison of Classification Strategies in Genetic Programming with Unbalanced Data

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

@InProceedings{conf/ausai/BhowanZJ10,
  title =        "A Comparison of Classification Strategies in Genetic
                 Programming with Unbalanced Data",
  author =       "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
  booktitle =    "Australasian Conference on Artificial Intelligence",
  editor =       "Jiuyong Li",
  year =         "2010",
  volume =       "6464",
  series =       "Lecture Notes in Computer Science",
  pages =        "243--252",
  address =      "Adelaide",
  month =        dec,
  publisher =    "Springer",
  bibdate =      "2010-11-30",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#BhowanZJ10",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-17431-5",
  DOI =          "doi:10.1007/978-3-642-17432-2_25",
  size =         "10 pages",
  abstract =     "Machine learning algorithms like Genetic Programming
                 (GP) can evolve biased classifiers when data sets are
                 unbalanced. In this paper we compare the effectiveness
                 of two GP classification strategies. The first uses the
                 standard (zero) class-threshold, while the second uses
                 the best class-threshold determined dynamically on a
                 solution-by-solution basis during evolution. These two
                 strategies are evaluated using five different GP
                 fitness across across a range of binary class imbalance
                 problems, and the GP approaches are compared to other
                 popular learning algorithms, namely, Naive Bayes and
                 Support Vector Machines. Our results suggest that there
                 is no overall difference between the two strategies,
                 and that both strategies can evolve good solutions in
                 binary classification when used in combination with an
                 effective fitness function.",
  affiliation =  "School of Engineering and Computer Science, Victoria
                 University of Wellington, New Zealand",
}

Genetic Programming entries for Urvesh Bhowan Mengjie Zhang Mark Johnston

Citations