Feature generation using genetic programming with comparative partner selection for diabetes classification

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

@Article{Aslam:2013:ESA,
  author =       "Muhammad Waqar Aslam and Zhechen Zhu and 
                 Asoke Kumar Nandi",
  title =        "Feature generation using genetic programming with
                 comparative partner selection for diabetes
                 classification",
  journal =      "Expert Systems with Applications",
  volume =       "40",
  number =       "13",
  pages =        "5402--5412",
  year =         "2013",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2013.04.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417413002406",
  abstract =     "Abstract The ultimate aim of this research is to
                 facilitate the diagnosis of diabetes, a rapidly
                 increasing disease in the world. In this research a
                 genetic programming (GP) based method has been used for
                 diabetes classification. GP has been used to generate
                 new features by making combinations of the existing
                 diabetes features, without prior knowledge of the
                 probability distribution. The proposed method has three
                 stages: features selection is performed at the first
                 stage using t-test, Kolmogorov-Smirnov test,
                 Kullback-Leibler divergence test, F-score selection,
                 and GP. The results of feature selection methods are
                 used to prepare an ordered list of original features
                 where features are arranged in decreasing order of
                 importance. Different subsets of original features are
                 prepared by adding features one by one in each subset
                 using sequential forward selection method according to
                 the ordered list. At the second stage, GP is used to
                 generate new features from each subset of original
                 diabetes features, by making non-linear combinations of
                 the original features. A variation of GP called GP with
                 comparative partner selection (GP-CPS), using the
                 strengths and the weaknesses of GP generated features,
                 has been used at the second stage. The performance of
                 GP generated features for classification is tested
                 using the k-nearest neighbour and support vector
                 machine classifiers at the last stage. The results and
                 their comparisons with other methods demonstrate that
                 the proposed method exhibits superior performance over
                 other recent methods.",
  keywords =     "genetic algorithms, genetic programming, Pima Indian
                 diabetes, Comparative partner selection",
}

Genetic Programming entries for Muhammad Waqar Aslam Zhechen Zhu Asoke K Nandi

Citations