Improving genetic programming classification for binary and multiclass datasets

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Al-Madi:2013:SSCI,
  author =       "Nailah Al-Madi and Simone A. Ludwig",
  title =        "Improving genetic programming classification for
                 binary and multiclass datasets",
  booktitle =    "IEEE Symposium on Computational Intelligence and Data
                 Mining, CIDM 2013",
  year =         "2013",
  editor_ssci-2013 = "P. N. Suganthan",
  editor =       "Barbara Hammer and Zhi-Hua Zhou and Lipo Wang and 
                 Nitesh Chawla",
  pages =        "166--173",
  address =      "Singapore",
  month =        "16-19 " # apr,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Computation, Classification, Multiclass, Binary
                 Classification",
  DOI =          "doi:10.1109/CIDM.2013.6597232",
  size =         "8 pages",
  abstract =     "Genetic Programming (GP) is one of the evolutionary
                 computation techniques that is used for the
                 classification process. GP has shown that good accuracy
                 values especially for binary classifications can be
                 achieved, however, for multiclass classification
                 unfortunately GP does not obtain high accuracy results.
                 In this paper, we propose two approaches in order to
                 improve the GP classification task. One approach (GP-K)
                 uses the K-means clustering technique in order to
                 transform the produced value of GP into class labels.
                 The second approach (GP-D) uses a discretization
                 technique to perform the transformation. A comparison
                 of the original GP, GP-K and GP-D was conducted using
                 binary and multiclass datasets. In addition, a
                 comparison with other state-of-the-art classifiers was
                 performed. The results reveal that GP-K shows good
                 improvement in terms of accuracy compared to the
                 original GP, however, it has a slightly longer
                 execution time. GP-D also achieves higher accuracy
                 values than the original GP as well as GP-K, and the
                 comparison with the state-of-the-art classifiers reveal
                 competitive accuracy values.",
  notes =        "CIDM 2013,
                 http://www.ntu.edu.sg/home/epnsugan/index_files/SSCI2013/CIDM2013.htm
                 also known as \cite{6597232}",
}

Genetic Programming entries for Nailah Al-Madi Simone A Ludwig

Citations