Cost-Sensitive Classification with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Jin Li and Xiaoli Li and Xin Yao",
  title =        "Cost-Sensitive Classification with Genetic
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "3",
  pages =        "2114--2121",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2005.1554956",
  size =         "8 pages",
  abstract =     "Cost-sensitive classification is an attractive topic
                 in data mining. Although genetic programming (GP)
                 technique has been applied to general classification,
                 to our knowledge, it has not been exploited to address
                 cost-sensitive classification in the literature, where
                 the costs of misclassification errors are non-uniform.
                 To investigate the applicability of GP to
                 cost-sensitive classification, this paper first reviews
                 the existing methods of cost-sensitive classification
                 in data mining. We then apply GP to address
                 cost-sensitive classification by means of two methods
                 through: a) manipulating training data, and b)
                 modifying the learning algorithm. In particular, a
                 constrained genetic programming (CGP), a GP based
                 cost-sensitive classifier, has been introduced in this
                 study. CGP is capable of building decision trees to
                 minimise not only the expected number of errors, but
                 also the expected misclassification costs through a
                 novel constraint fitness function. CGP has been tested
                 on the heart disease dataset and the German credit
                 dataset from the UCI repository. Its efficacy with
                 respect to cost has been demonstrated by comparisons
                 with noncost-sensitive learning methods and
                 cost-sensitive learning methods in terms of the
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Jin Li Xiaoli Li Xin Yao